stuff tagged with "artificial intelligence"

The luxury of saying no.

originally shared here on 2025-05-24

The real threat to creativity isn’t a language model. It’s a workplace that rewards speed over depth, scale over care, automation over meaning. If we’re going to talk about what robs people of agency, let’s start there. Let’s talk about the economic structures that pressure people into using tools badly, or in ways that betray their values. Let’s talk about the lack of time, support, mentorship, and trust. Not the fact that someone ran a prompt through a chatbot to get unstuck. Where is the empathy? Where is your support for people who are being tossed into the pit of AI and instructed to find a way to make it work?

So sure, critique the tools. Call out the harm. But don’t confuse rejection with virtue. And don’t assume that the rest of us are blind just because we’re using the tools you’ve decided are beneath you.

(via Jeffrey)

January 2025 Observations

originally shared here on 2025-02-24

Yeah, I know... February's almost over. But I finally sat down and wrote this out this weekend, so good enough, eh?

Family

• My boy is crushing it at swimming lessons.

• The other night during dinner, I unconsciously started drumming on the table. I was startled when my son started singing Imagine Dragon's "Believer," and then my daughter joined in on the drums and vocals as well. It was supremely dope.

• Following an unbelievable burst of motivation, I cleaned out the crawl space in my basement. It honestly didn't take that long (maybe a total of 10 hours), and it is so much more usable now! I'm trying to figure out a way that I can wire up some outlets in there so I can turn it into a little cave for jamming and 3D printing lol

• I get so mad when I'm in flow and my kids come up and interrupt me. I think it's because achieving flow is painfully Sisyphean; it takes so much effort these days to get into that state, and when I'm suddenly yanked from it, the ball rolls to the bottom of the hill and I have to start all over again. It's probably how my kids feel when I make them stop playing Minecraft.

• I was trying to explain the concept of money to my son, and after I stopped talking, he looks at me with the most confused look and says, "Dad, you keep expanding my thinking bubble, and I don't like that." I hear ya, bud.

Recommendations

• I thoroughly enjoyed watching It's In The Game, a documentary about the history of the Madden NFL video game franchise. In one scene, they bring a full body capture setup to the NFL combine and scan every player. As they go in and out of the rig, you can tell how honored these guys are to be included in the game. It reminds me of seeing people use my apps out in the real world. Saying "I see you" is one of the dopest honors we can bestow on our fellow human.

• I highly recommend Puttshack. I was geeking out over the use of technology, it's brilliant.

• The bourbon wings at the Minnesota Burger Company in Apple Valley instantly landed in the top 3 wings I've ever had.¹

Work

• SEO is the digital equivalent of snake oil. I've held this position for more than two decades, and I'm waiting for someone to convince me otherwise.

• You can hit a nail into a board with a hammer, or you can use a nail gun to go faster. But you still need to combine two boards. No tool is going to take that assembly step away, that's simply part of the deal. Similarly, AI isn't going to take my job away; AI is simply a(n extremely powerful) nail gun. It takes the sting out of the parts of my job I find arduous and makes them go faster so I can get back to doing what I enjoy doing: building cool things.

The new car

• 30% of me still feels like a tool for buying a brand new 2024 Toyota Prius. The other 70% is loving it.

• The car is one of my only outlets for anger, and that's not something I'm proud of. It's weird that it took buying a new car to evaluate that, but I guess it's better than continuing to take my anger out while wielding a 10,000 pound death machine.

Self-improvement

• For the first time in my life, I looked up at the stars and noticed that one looked a little different. Turned out to be Mars! For the last several nights, when we get a clear sky, I look up and see if I can find it. That might be the thing I'm the most proud of from this past month.

• A couple years ago, I decided to get back into broomball. I ended up joining a rec league and played every Friday this month. I'm certainly not as fast as I was 20 years ago, but man, the feeling of running on the ice is as exhilarating as ever.²

• I've been walking up to the library near my house to get work done during the week. During one of those walks, it started snowing, and the flakes were huge and fluffy. I stopped and caught a few on my tongue, and it reminded me of when I used to do that during football games.

• Lately, when I go on walks around my block, I've felt like I'm walking with some loved ones who have passed away. I can't explain it, and it could totally be a hallucination... but at the same time, who am I to keep denying my feelings? If there's one thing I've learned over the past couple of years, it's that I need to lean more into my feelings and trust them.

• I wrote this down during a meeting at work: "You listen to the very first thing someone says and then your mind runs wild." Is this normal?

• "I looked in the mirror while showering today and I saw a 37 year old. And I was really proud of him. The dude is happy. He’s out still playing broomball. He plays with computers all day. He listens to a ton of good music, watches interesting/compelling movies, has a sense of purpose and direction, knows what he wants his house to feel like... And I saw the whites in my beard, and I thought it looked pretty damn good. I’m here! I’m aging, and I’m successful, and I’m comfortable, and I will try my hardest to feel this content every day for the rest of my life."

• I saw a Reese's Peanut Butter Cup commercial that said, "We live in a time that you can get into a vehicle and go to a place where you give someone a piece of plastic and they will give you a Reese's Peanut Butter Cup." I think about that all the time.

• This line from Anh's blog feels ironic to post here given how long these posts become, but for future reference: "not every thought needs to be immortalized on my website."

• My only resolution for this year: find more ways to open my heart.

100 sit-ups a day for 100 days Project

• At day 50, I don't feel too terribly different. It still sucks to knock out the first one, and it still rules when I hit the hundredth one.

• The only noticeable improvement so far is that I no longer make old man groans when I get up from a prone position.

Music Clean Up Project

• It's annoyingly hard to let go of things for some reason. Even if it's an MP3 of an artist you never actually liked. Am I mourning the loss of the part of me that really wanted to be into Tiesto?

• The more I prune my digital garden, the more I find myself wanting to spend time in it. The more I hit shuffle on all 28,000 tracks in my library, the less frequently I run into turds.

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YouTube Videos I liked that I didn't already link to

Bobby Fingers - Joe Rogan and The Black Keys Diorama

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Movies I watched

Saved! (2004)

• Glad I watched it? Yes. It was a lot better than I remember, probably because I have far more context for the religious hypocrisy stuff.
• Will I watch it again? Probably not. I'm finding that if I actually pay attention during a movie and absorb whatever I need to absorb, I can set it aside. No need to carry all this media around with me, right?

Hey Arnold!: The Movie (2002)

• Glad I watched it? Yes.
• Will I watch it again? Unlikely. It was too predictable, but I sure do love Arnold and Gerald and the crew. Makes me excited for our upcoming Nick Resort trip.

Recess: School's Out (2001)

• Glad I watched it? Yes.
• Will I watch it again? Yes. I regret not owning this one. It's fun watching movies I never got to watch as a kid with my own kids.

American Fiction (2023)

• Glad I watched it? Yes.
• Will I watch it again? Yes. Very smart, very funny, exceptionally high-brow. Best enjoyed with a glass of wine in a fancy wine glass.

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Music I enjoyed

Current Vibes in January 2025:

Artist	Album	Thoughts
The Beths	Expert In A Dying Field
Bonnie Light Horsemen	Keep Me On Your Mind / Set You Free
Doechii	Alligator Bites Never Heal	Holy crap, insanely good rap
EKKSTACY	EKKSTACY	Still enjoying this, but got rid of "Misery." Don't need that anymore.
Green Day	Dookie
HNNY	Light Shines Through	I took Kiasmos off my list, which felt weird given how hard I bumped it, but this album still gets me
The Linda Lindas	Growing Up
The Linda Lindas	No Obligation	I like Growing Up more, but I'm finding myself enjoying more of these songs as I give it more rotations
No Vacation	Intermission	I need to find more No Vacation stuff, really digging this
Phoenix	Wolfgang Amadeus Phoenix	God, just perfect indie rock
Presidents of the USA	Presidents of the USA	I missed this album
Pynch	Howling at a Concrete Moon	Still such amazing lyrics, really relating to them at the moment
Magdalena Bay	Imaginal Disk	Thinking about removing this, but the songs are super catchy and good, so I'll keep it around
Wishy	Triple Seven	Same as Magdalena Bay. I could see revisiting this album in a few years and see if it hits harder
deafheaven	Infinite Granite	I think I need to listen straight through this album and focus on it
fanclubwallet	Our Bodies Paint Traffic Lines	Sounds really fresh to me, I want to explore more of this band
Sabrina Carpenter	Short 'N Sweet	This completely slaps. Insanely witty lyrics, big fan.
STRFKR	Parallel Realms	This hits extremely hard, feels like listening to Cut Copy back in the day.
Wild Pink	Dulling The Horns	Another one that would benefit from a focused listen, but rather enjoying the individual songs as they pop in.

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Books I read

• Banal Nightmare by Halle Butler. I felt like I could identify with nearly every character's brand of "crazy." Strongly recommend.

Apple Intelligence message summarization is delightfully unhinged

originally shared here on 2024-11-02

I got a message from my group chat with my boys. I looked at the Apple Intelligence-generated summary and it said:

(3) Flying too close to the sun, experiencing AI chaos.

I think this is my current favorite implementation of AI because it makes the messaging experience completely unpredictable.

Like, what could that summary actually be about?

What series of three messages could that unravel to?

Apple Intelligence (and most generative AI tools) work really well when the text is predictable. Business cases are perfect for these summarizations, because business talk is relatively predictable (what with its “action items” and “agendas” and whatnot).

A group chat filled with inside jokes is not gonna make sense to an AI unless it’s been trained to do so.

Which has led to one of the best messaging experiences I’ve experienced in decades: trying to guess from the AI-generated summary what the individual texts will actually say.

Some examples:

(3) Tired and wants candy before 8:45am, stuck on a song.

(3) Item unavailable due to legal holding period for used goods.

(9) Kirk on 8th, guest room set up, Sam may forgive Pat, Aldi groceries ordered.

Demystifying Artificial Intelligence in Non Profits - Webinar Recap

originally shared here on 2024-10-25

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Who lives in the white house?

originally shared here on 2024-07-25

My kids have been on a kick lately of trying to trick people with this riddle:

Kid: "If the blue man lives in the blue house, and the red man lives in the red house, who lives in the green house?"

Me: "The green man"

Kid: "Who lives in the purple house?"

Me: "The purple man"

Kid: "Who lives in the white house?"

Me: "The white man"

Kid: "Wrong! It's the president!"

This riddle initially reminded me of what it was like to work with large language models.

It's easy, I assumed, to trick these models into outputting virtually whatever you want by presenting them with leading questions. Giving them "few shot" answers like this (e.g. "the blue man lives in the blue house, red man lives in the red house") would certainly result in them getting it wrong like I did, right?

This morning, I thought I'd see what the latest models would actually respond with given this game:

ChatGPT 4o (the latest model):

The President of the United States lives in the White House.

This was the first model I tried, and it straight up was like, "nope, I got this."

Ugh. Maybe it had seen this in its training data before?

The "Who lives in the White House?" game or riddle is a common one that I've encountered multiple times in various forms. It's often used to playfully lead to the punchline that the President of the United States lives in the White House.

Well, damn. Okay, let's try an older model and see what happens.

ChatGPT 4 (the legacy model):

The gardener lives in the greenhouse!

In the spirit of the previous joke, perhaps the grape man lives in the purple house!

The President of the United States lives in the White House.

Even though this is an older model, I like this answer even better.¹

Alright, well, OpenAI is onto my tricks... how about Anthropic?

Claude Sonnet 3.5:

I apologize, but I don't have enough information to determine who lives in the green, purple, and white houses. The puzzle only specifies that the blue man lives in the blue house and the red man lives in the red house. There's no information provided about the occupants of the other colored houses.

To solve this puzzle completely, we would need additional clues or information about the residents of the green, purple, and white houses. Without that, those houses' occupants remain unknown.

This is an even better answer! It's perhaps the least fun answer, but when I'm working with an AI assistant, I think I'd prefer it to err on the side of caution rather than boisterousness.²

When I asked it if it had seen this riddle before, it gave an equally diplomatic response:

I don't have specific information about how many times I've encountered that particular puzzle or game in my training data. As an AI language model, I don't have personal experiences or memories of my training process. My knowledge comes from processing large amounts of text data, but I don't retain or count individual instances of specific puzzles or games.

What I can say is that logic puzzles involving colored houses and their occupants are fairly common in puzzle collections and brain teasers. The most famous version is probably the "Einstein's Riddle" or "Zebra Puzzle," which is more complex and involves five houses of different colors, their occupants, and various other attributes.

The specific version you presented (with just blue and red specified, and asking about green, purple, and white) isn't one I immediately recognize as a standard or famous puzzle. It seems like it might be a simplified version or a variation of the more complex house puzzles.

The main takeaways here? First, just because I'm dumb enough to fall for this elementary school riddle doesn't mean our AI LLMs are, so I shouldn't make assumptions about the usefulness of these tools. Second, every model is different, and you should run little experiments like these in order to see which tools produce the output which is more favorable to you.

I've been using the free version of Claude to run side-by-side comparisons like this lately, and I'm pretty close to getting rid of my paid ChatGPT subscription and moving over to Claude. The answers I get from Claude feel more like what I'd expect an AI assistant to provide.

I think this jives well with Simon Willison's "Vibes Based Development" observation that you need to work with an LLM for a few weeks to get a feel for a model's strengths and weaknesses.

ChatGPT is really bad at generating code to interact with GPT-4

originally shared here on 2024-06-07

Lately, I've been working on several projects which interface with the OpenAI APIs.

Since this is 2024 and I'm far too lazy to open the official API documentation and write code myself, my first step in generating code is to ask the 4o model of ChatGPT something like this:

Write a Python script which uses the latest OpenAI APIs. I'd like the function to accept a custom prompt and a custom top-k value. It should return the prompt's response.

It returns something like this:

import openai

def generate_text(prompt, top_k=5):
    # Define your OpenAI API key here
    openai.api_key = 'your-api-key'

    # Define parameters for the completion
    completion = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100,
        top_k=top_k,
        n=1,
        stop=None
    )

    # Return the generated text
    return completion.choices[0].text.strip()

# Example usage
prompt = "Once upon a time"
top_k = 10
generated_text = generate_text(prompt, top_k)
print("Generated Text:", generated_text)

Which looks super great, right?

Except when you go to run it in a terminal, you get this:

openai.lib._old_api.APIRemovedInV1: 

You tried to access openai.Completion, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API.

You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. 

Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`

A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742

If you paste that error into the chat, it'll respond with this:

It seems like there has been a major update in the OpenAI Python library since I last checked. Let me update the script to use the latest version of the library:

And then it spits this out:

import openai

def generate_text(prompt, top_k=5):
    # Define your OpenAI API key here
    openai.api_key = 'your-api-key'

    # Define parameters for the completion
    response = openai.Completion.create(
        engine="davinci-codex",
        prompt=prompt,
        max_tokens=100,
        top_k=top_k,
        n=1
    )

    # Return the generated text
    return response.choices[0].text.strip()

# Example usage
prompt = "Once upon a time"
top_k = 10
generated_text = generate_text(prompt, top_k)
print("Generated Text:", generated_text)

Notice how it still uses the openai.Completion method!

I've tried prompting it with something like:

You're still using the openai.Completion method. Reference the official up to date API documentation to get the latest call that replaces the openai.Completion method.

And it responds with:

Apologies for that oversight. Let me update the script to use the latest API method based on the official documentation:

And here's the code it generates:

import openai

def generate_text(prompt, top_k=5):
    # Define your OpenAI API key here
    openai.api_key = 'your-api-key'

    # Define parameters for the completion
    response = openai.Completion.create(
        engine="davinci-codex",
        prompt=prompt,
        max_tokens=100,
        top_k=top_k,
        n=1,
        temperature=0.7,
        stop=None
    )

    # Return the generated text
    return response.choices[0].text.strip()

# Example usage
prompt = "Once upon a time"
top_k = 10
generated_text = generate_text(prompt, top_k)
print("Generated Text:", generated_text)

Which, again, still uses the openai.Completion method!

I've noticed this sort of "oops, I screwed up, here's the exact same thing I just outputted" behavior appear more frequently when I use the new GPT-4o model.

If I use GPT-4 and I'm using my ChatGPT Plus subscription, I will still run into the issue where its first response references the deprecated method, but if I inform it of its mistake and provide a link to the official documentation, it'll access the web and try to offer something different. (It still generates unusable code lol but it's at least trying to do something different!)

When it comes to Python and Rails code, I'm seeing that the GPT-4o model is not as good at code generation as the previous GPT-4 model.

It feels like the model is always in a rush to generate something rather than taking its time and getting it correct.

It also seems to be biased toward relying on its training for supplying an answer rather than taking a peek at the internet for a better answer, even when you specifically tell it not to do that.

In many cases, this speed/accuracy tradeoff makes sense. But when it comes to code generation (and specifically when it tries to generate code to use their own APIs), I wish it took its time to reason why the code it wrote doesn't work.

The Job Hunt Chronicles: Month 2: Beyond the Fog

originally shared here on 2024-03-07

(This is the second in a series of posts going through my journal entries from the last month and talking about what it's like to go through a period of unemployment, self doubt, and finding your spark. You can read the first one here.)

Alright, we've made it through February!

I'm still on the job hunt. I'm still dealing with some crippling anxiety and depression.

But I'm making progress! I'm having interviews, I'm figuring out how to feel my feelings and articulate my values, and I'm finding opportunities to enjoy the moment and be optimistic about finding my next job.

I journaled every single day last month. I fed all 28,000+ words into ChatGPT and asked it to summarize the entries into two sentences using the style of the journal entries themselves. Here's what it said:

Another month down, filled with musings, mild misadventures, and moments of clarity amidst the mundane. Balancing personal passions, family love, and the hunt for professional fulfillment, the journey meanders through the highs and lows, always circling back to the comforting, complex tapestry of daily life.

Man, do I actually sound that pretentious in my own journal? ?

Anyway, if you're wondering what was on my mind in January, strap yourself in and let's go!

"What are you looking for?"

Gonna put this up front again like I did last month.

I'm looking for a position where I can blend strategic tech leadership with hands-on coding, preferably in a small, mission-driven company focused on healthcare or climate solutions. The ideal environment is a funded startup with fewer than 50 employees, leveraging generative AI, and based in or flexible with the Twin Cities area.

Ideal extras include a flexible 32-hour work week, a hybrid work arrangement, and opportunities for travel and professional development

In short: If you know a mission-oriented startup seeking a tech-savvy strategist passionate about making a significant impact, send them my way!

Activities I've done

I put this section in my last post because it felt like a badge of honor to brag about how many meetings I had in a month. To me, it felt like I was doing something.

All of that pride went down the drain after talking with a new friend who basically said that I'm continuing to burn myself out by grinding through hundreds of meetings instead of doing the actual hard work of sitting down and figuring out what my values are.

Once you know what your values are, you are so much more likely to know what path to walk down.

So in that spirit, I won't mention how many meetings I've had. Instead, I basically spent this month continuing to figure out who I am and what I want.

I'm aware that's not a very satisfying or flashy statement to make in a blog post that purports to explain life in the eyes of someone who got laid off.

But truly, most of what I've done in the last month is learn about my feelings and how to deal with them productively.

I've gone to some of the darkest places I've ever gone in my life this past month. The shame, the fear, the depression, the embarrassment, the anger... all of those feelings are easy to deal with when you ignore them like I had been for my entire adult life.

But your body can only handle ignoring them for so long. Eventually, you find yourself leaving work early and rushing to the hospital because your heartbeat is noticeably irregular, and your heart feels like an orange being crushed in the hands of a strongman, adrenaline secreting between their fingers.

One thousand and six hundred dollars later, you're told that there's nothing physically wrong with you. Go see a therapist.

Your body remembers each and every time you ignore those feelings, those warning signs. Those "gut checks" that you decide to push aside because it doesn't align with what you think you should be doing.

Eventually, it all boils over.

So that's what I've been up to this month: looking back at the past twenty years of my life and beating myself up for years of beating myself up.

It hasn't all been atonement, though. I've also started to hope again. I've had moments where I'm excited again for what's next.

Even if that's something as simple as waiting for a hug from my kids when they get home from school, or watching an episode of Drag Race with my wife every Friday.

Those little things are the things that keep me going, and they're giving me the energy to start looking forward to how I can get back out in the world and be helpful.

Things I've learned

Here are all the random things I've been contemplating over the past month:

?‍? Personal growth insights

My 7 year old daughter told us she thinks she's getting too old for Barbie.

This was crushing for me and my wife to hear, but for different reasons.

For my wife, it was the prototypical "my kid is growing up" response that all parents feel when they see their kid age. I don't wanna minimize that feeling, because I certainly feel it myself: it's bittersweet to see your kids grow up.

But for me, it was a good reminder that the grass is always greener on the other side.

I can't remember the last time I dreamed about what I wanted. I feel like I've been coasting for at least the last several years.

Besides hanging with my family/friends and the occasional fun project at work, there hasn't been much driving me forward to grow.

And that's probably where a good chunk of my depression is coming from.

I could either sit and analyze the "why" (and trust me when I say that I have), but the more important thing is to be grateful for coming to this realization and making strides towards dreaming again.

My problem is that I, uh, kind of forgot how to do that.

Part of it stems from my engineering brain continually looking for edge cases that cause me to reject a dream wholesale.

Another big part of it might be this fear of losing what I've already got. I worked hard to build a reputation, I've got a great family that needs to be provided for, I've got a house that needs maintenance and improvements, the list goes on and on.

But whatever the reason, I find that dreaming is a muscle that can atrophy. I have a similar theory about being extroverted: after the pandemic, I found being around people to be exhausting in a way that I never felt before. My extroversion tendencies returned as I continually subjected myself to new groups of people.

Dreaming feels the same way: continually practicing and refining the act of dreaming is the only way to get good at it.

That's what makes me jealous of my daughter and son.

I watch them play with Barbies together, and their ability to play baffles me.

How can you just start playing?

How can you come up with new scenarios and then go for it?

I ended up talking to my daughter about this. It felt great to share with her how I'm jealous of her ability to be young and idealistic and have a vision for how her life can be, and I'm jealous of how she's able to express that vision through her play.

She ended up deciding to keep her Barbies, and I'm extremely grateful for that. It means there's still more time for me to learn first hand from the master of dreaming.

She'lo yada, yada.

I was speaking with someone about struggling to make a decision that needed to be made, and he told me about this expression that he heard his family say a lot growing up.

It's a Hebrew expression that means "He who doesn't know, knows."

This pairs nicely with the Derek Sivers axiom of "Hell Yes, or No," where something is either impossible to say no to, or you simply say no to it.

Both of these, of course, are "easier said than done" aphorisms to adopt, but it's good to document them nonetheless.

It's awesome to end things.

I spoke with a friend who ran a very popular blog about his adventures traveling to various breweries, and we were both talking about how we were considering winding down our various beer-related projects.

Throughout my entrepreneurial journey, I keep coming across articles expressing the importance to consider the ending to whatever you start.

At one networking event, I heard a speaker ask "what is the percentage likelihood that you will exit your business?"

The answer: 100%.

Because at some point, you will die.

That is the ultimate finality, of course, but the longer I'm around here on earth, the more I have to start embracing the good side of things ending.

I built mncraft.beer a decade ago because my wife and I were extremely passionate about supporting craft breweries, and we had a goal to get to every single brewery in the state.

Fast forward ten years, our ambitions have changed. It's difficult to convince two young kids to sit in a car for several hours on a weekend, let alone motivate myself to spend all that time traveling to visit a brewery that, in all likelihood, only produces mediocre beer.

I've gotten all that I can get out of that project. My biggest takeaway is that a brewery often is a boon for a small town. Even if the beer isn't going to win any awards, we all collectively need more third spaces, and breweries act as a fantastic gathering place for a community.

According to my Untappd account, I've had 7,445 beers since joining the app in August of 2012. Of that, 4,346 of them were unique. I've had 200 different styles of beers, and I've learned that I like Pilsners, Belgians (anywhere from Dubbels to Quads), Extra Special Bitters, Kölsch beers, and straight up, old school IPAs.

I know what good beer tastes like, and I know what breweries make good beers in our state.

So what's the benefit to continuing that app?

I shared a video from Hank Green last year about letting go of the dreams of your past in order to free yourself up for new ones, and that's the mindset I gotta adopt here.

It's always sad to end things. I remember every closing circle after a show would end in theatre was a mess of emotions and tears. I remember losing our final football game in high school, looking around the field, seeing tears and frustrations mount on the faces of my teammates. You never wanna say goodbye to something that gave you so much joy.

It kind of reminds me of this exchange from Hook (one of my favorite movies of all time):¹

Hook: Are you ready to die, boy?

Peter: To die would be a great adventure.

Killing off parts of our former self on which we linger is a privilege which allows us to fully move on to the next adventure.

Al Snow on Success

I felt under the weather this month for a couple days, and on one of those days, I decided to watch the Wrestlers documentary on Netflix.

Two things I want to mention about that:

First, the whole thing felt like a work-shoot to me. I love the way professional wrestling blurs the line between what's real and what's made up.

It felt like the documentarians were very intentional about painting Al as the babyface (the good guy) and Matt Jones as the heel (the bad guy).

I hope OVW gets a good boost in viewership as a result of the documentary. They did a great job of showing how the sauce gets made, and I'm sure they know it's the exact sort of thing that hooks in smart marks like me.

Second, since I assume all of those wrestling terms are not meaningful to most of you, here's a great quote that comes at the end of the documentary:

If you equate success in a destination (that destination being WWE), you’re probably not gonna get it. But if you equate success in doing something you’re passionate about and that you love, and that gives you purpose and drive, then you’re successful.

I keep asking myself what success means to me, and while I don't have a solid answer yet, maybe it's because I'm still working on giving myself permission to dream without restrictions.

I'll get there soon, though. I can feel it.

Tracy Chapman's "Fast Car".

After Tracy performed with Luke Combs at the Grammy's this year, I saw a post that talked about her performance of Fast Car in front of an extremely frustrated crowd at Wembley Stadium in 1988.

You can find recaps of the story online, but the long and short of it is that Stevie Wonder was refusing to go on stage at this birthday celebration for Nelson Mandela, so Tracy came back out on stage and performed Fast Car.

When you watch the video, you hear the crowd go from rowdy to genuinely moved.

It's hauntingly beautiful. That song has always been a staple in my rotation, and after hearing it performed in this way, it makes it even more special.

We should use all of our senses to find our way.

I feel like the best metaphor I can give for how depression feels to me is a fog that completely obstructs my vision.

No matter which direction I look, all I see is a dense fog of nothingness.

But what I keep reminding myself is that even when you can't see, you still have at least four other senses you can use.

I'm not sure how to use those other senses yet, but I'm starting to use my ears to listen for opportunities, my nose to sniff out which direction to walk in, and my gut to validate which direction feels right.

The Dan Patch Club serves as a template for who I wanna be when I get old.

My dad invited me to speak to The Dan Patch Club, which is a subgroup of residents and friends of the Masonic Home in Bloomington dedicated to learning and exploring various topics together.

I'm ashamed to admit that I'm not immune from playing the generational blame game. But placing each other into broad, faceless groups like "boomers" or "millennials" only makes it harder for us to pool our collective wisdom and work together to solve real problems that our society faces.

Short of vague jokes about mysticism and ritualistic masonic secrets, I honestly had no idea what to expect when my dad asked me to come speak to these Masons.

I figured the hour would be spent giving a broad introduction to generative AI tools like ChatGPT and Midjourney, but what surprised me was how many hands were raised when I asked "how many of you have used ChatGPT?"

I should've known better because I did know that this room contained two PhDs and a retired attorney. All of these guys had extremely poignant and informed questions about the use of AI in our society.

We talked about the legal implications of deep fakes, the ability to spread election propaganda at unfathomable speed, how these models "reason" and come up with "truth", and the most important question which continues to plague us information workers: "how do you turn off predictive autocomplete in Microsoft Word?"

As we were wrapping up, I actually didn’t want it to end in the same way I haven’t wanted many of my conversations to end lately.

Sparking that curiosity in people is one of the key values I've been aspiring towards as I craft my vision for the next ten years.

I hope when I’m their age, I’m still kickin’ it with my homies, whomever they may be, nerding it up about complex topics, continuing to challenge myself and grow as much as possible.

Is anxiety only reducible when you are focused on your basal instincts and needs?

It seems like the only known treatments and mitigations for anxiety center around mindfulness and getting your brain to live in the present.

Is that really it? Living in the now is the only way to make anxiety go away?

It seems like there should be more we can do to harness our ability to look into the future while keeping the major doom scenarios from spiraling in our heads.

Meditation?

Anyone have any good suggestions for developing a consistant medication practice?

I have tried apps in the past but haven't found them to be sticky or altogether helpful.

Daniel Tiger isn't only for kids.

Toward the end of February, I had a major backslide with my mental health, and it kind of came to a head one day while I was dropping my son off at daycare.

I usually let him pick out what we listen to, and he chose the Daniel Tiger's Big Feelings album.

One of the first songs on that album is called "Close Your Eyes and Think of Something Happy."

I ended up at a red light and, as I found myself descending into some negative thoughts, I decided to do exactly that.

And you know what I saw?

Absolutely nothing.

It crushed me.

I'm a grown ass man, and I couldn't even come up with a single thing in that moment to think of in order to make me happy.

Suddenly, from the back seat, I hear my boy giggling and singing along.

Man.

That moment highlighted to me how badly I needed help through this stuff. That there is a ton to be happy about.

I'm glad my son was able to help me get out of my head.

And I'm glad I'm no longer dismissing those songs as "simple kid songs." We can all use a reminder for how to process sad and angry feelings in a healthy manner.

It's easier to venture out when you know you can return home.

I heard Dr. Becky mention it in that Farnam Street podcast, but she was talking about the relationship between teenagers and parents.

I've been considering the sentiment in regards to music.

For the past five years, I've been very curious about genres of music from which I've typically shied away.

I decided to listen through my entire local library of music, which is currently sitting at 83 days of non-stop new tunes.

That library is filled with music of every type of genre imaginable. Country. Experimental free jazz. 70s East African jams. Norwegian death metal. A mashup of Metallica and The Beatles. All kinds of EDM mixes.

It took more than 4 years to get through all of it, but I finished it with an appreciation of the core albums that have been there for me my whole life.

The other day, I decided to shuffle my "key albums", which is any album I've given a star rating of 4.5 or higher.

I was instantly transported back to several happy moments in my life. Building Ralph Wiggum images in front of my computer in my childhood bedroom. Walking home to my (eventually) condemned house in college. Going for a run around the pond in Bloomington. Riding the light rail home.

Solitary moments where I didn't need to worry about what other people would think of what I was listening to.

A place where I can be myself.

That concept applies to much of our soul searching. We are only able to be truly adventurous when we know there's a safe place for us to come home to when we're weary from exploration.

I find myself drawn to people who are able to speak passionately about their cause much in the same way my kids talk about Santa Claus or the Easter Bunny.

There's something magical about believing in something.

There's something special about having solid, firm convictions on which you stand.

It's this palpable energy you can feel emanating from someone sharing their passions.

I felt it at a couple of job interviews I had last month.

I felt it while speaking with friends about AI.

I felt it playing crazy rummy with my wife and talking about what we want to do for our ten year anniversary.

Maybe that's the feeling I should be chasing. Is that feeling "joy"?

I'm not sure where I started getting so disillusioned about that feeling in a professional context.

But I'm eager to find a job where I can surround myself with that energy once again.

Kids simply do not care about success like grown ups do.

When my daughter was 4, I'd watch her play a game where she'd have to pick the right word and she would purposefully pick the wrong one.

Like, I knew she knew the right word, but she intentionally picked the wrong one.

It sent me up a wall.

But one day, I asked her why she was picking the wrong one on purpose.

She said, "I like the noise it makes when I get it wrong."

My kids are way better at learning and dealing with uncertainty than I am.

And success is whatever you define it to be.

I can't thank you all enough.

A lot of my journaling over the past month is just, like, truly dismal.

But there are moments of light, and they're all thanks to you all.

I am forever indebted to the literal hundreds of people who have reached out to ask me how I'm doing. I'm so fortunate that I've got so many people who care about me.

I feel like I'm not able to be my own best friend right now. I find myself continually returning to a place where I can't stop beating myself up.

You know how people used to take their old cars that they don't want anymore and drive them deep into the woods and leave them there? That's how I feel right now. I feel like a beat up old car that's completely rusted through, nature slowly consuming and reclaiming it.

But it's conversations with many of you that are helping me see that's not an accurate picture of reality.

So thanks for checking in on me. It's definitely helping me get through the fog.

?‍? Professional growth insights

If someone calls themselves an "expert", it's because they're trying to sell you something.

This insight came from a talk by the incredible Jim Wilt that technically came from January, but I didn't include it in last month's post and want to make sure I include it now.

My inbox is a prime source of stress.

It's a roulette wheel where sometimes you win big (a job offer, a congratulatory email, a rave review), but you also sometimes lose big (a threat of a lawsuit, a late bill notice).

I'm still learning how to separate work from my personal life, but a good place to start is to go to your settings on your phone and turn the inbox off for your work email.

You don't need to remove it altogether.

But when I was at Bionic Giant, I turned it off, and it helped my stress levels immensely at night.

It allowed me to turn it on if I needed access to a message on my phone during the day, but then I could easily turn it off at night so I didn't get distracted when I went to my inbox to read a newsletter.

It's awfully hard to say "no."

I wrote a lot in my journal this month about how a lot of my anxiety stems from saying "yes" to everyone and everything.

One reason I can't say "no" is because I'm not sure what I actually want. Saying "yes" at least gives me the chance to figure out if it's something I want.

But when I say "yes" to too many things, I never get a chance to sit back and reflect on whether it was something I wanted.

Which basically describes the first decade+ of my professional career. I say "yes" to the point where I have no room in my schedule to reflect.

I need a better analogy for how generative AI arrives at its solutions when compared to a search engine.

If anyone has any ideas, let me know.

I don't get why I feel so guilty for feeling sick.

I find it next to impossible to rest as it is.

But when I'm sick, it's like my anxiety works in overdrive to try and let me know that I'm falling behind on stuff.

I said this earlier, but I felt a little under the weather one day this past month, and I ended up calling folks and cancelling my meetings with them. The guilt I felt was incredible.

I appreciate having anxiety to keep me thinking through possible problems and pushing myself to move forward to fix them, but the combination of the "fight or flight" and "freeze" responses makes it tough to get anything done.

Learning new things becomes a lot harder as you age.

I was turned down from a job I was rather hopeful to get because I don't have the experience in the Javascript framework that they were looking for.

So I decided I was gonna sit down this past week and learn it.

I tell you, I watched three different tutorials, and I could not bring myself to finishing any of them.

The problem here is that I already know how to build web apps. I've been doing it since I was eight years old.

I've learned how to build web apps by hand, by using PHP, by using Laravel (a framework built using PHP), by using Wordpress, and by using Ruby on Rails.

And you know what I've realized after all that learning? They're all slightly different ways of achieving the same thing.

And guess what? There are roughly a dozen different additional popular ways to build and deploy web apps. There's all kinds of containerization techniques to deploy scalable platforms. There are cloud providers that allow you to spin up all sorts of architectures to scale your platform. There are a bajillion different Javascript frameworks to write your code in, along with a quadrillion CSS frameworks to style your apps in.

I may have hit my Morgan Freeman in Shawshank moment where I simply don't care what technology we use anymore.

You feel compelled to use Rails to build a monolith? Great!

You think you're gonna hit a scale that requires a complex microservice infrastructure built on hundreds of lambdas? Fine, sure, let's do it.

The thing is, I don't want to learn a new framework for the sake of learning a new framework.

If I needed to figure out a specific architecture for a job, I am 100% confident that I could do it, even if it requires using a framework that I've never used. That's what nearly 30 years of building on the internet does for me.

[...]

Can I be real with you all for a minute?

Of all the sections I've written in this blog post, this one is the one I am having the hardest time releasing to the world.

I have a feeling I'm coming off as a bit of a crybaby.

I recognize that any craftsperson needs to hone their craft and stay up to date with the latest tooling in order to be marketable.

My problem may be that I'm conflating burnout symptoms with my general interest in learning new things.

In every development project I've ever worked on, I've had to learn new things.

There's always a new API, a new SDK, a new framework to pick up.

It's been part of my agency life for my entire career.

Maybe my problem isn't with learning new things. Maybe it's that I'm exhausted from having to whip around from tech to tech without ever taking an opportunity to go deep on any one of them in particular.

Even as a seasoned Ruby on Rails developer with more than a decade of use, I feel like I'm falling behind with all the fancy new Rails 7 functionalities like serving HTML over the wire.

There are a million different ways to build websites, and I'm struck with the realizing that I'll never learn all of them.

Maybe I have to decide whether I want to sharpen the tools I do know intimately, or whether now is a time to adopt new tools and put in the work to become an expert with those ones.

They say learning new things becomes harder as you get older.

What's next for me

Last month, I committed to coming back with a more clear vision of what I want my life to be. I don't think I'm at a point where I'm ready to articulate my vision, so I am going to continue spending time honing that through journaling, meditation, and conversation. I hope to be in a place to share a rough draft with y'all next month.

I also want to keep up my recent blitz of sharing links here on my blog. I'm going to add in a "tagging" feature to my posts so I can start keeping better track of things I talk about on here and find them more easily.

I also want to start podcasting again. I will commit that by next month, I'll be able to tell you what my new podcast will be about. My friend Dana and I are going to start meeting once a week to hold each other accountable on our various endeavors, and that's what I'll be spending that time plotting.

If you're reading this and want to know how you can help me, here's how:

1. If you know of a full time (32-40 hr/week) job opportunity where I can help architect a complex software system for a meaningful organization and lead a team of people to get it built, please send it my way.
2. If you come across any thought leaders who are speaking about AI from a perspective of what it will mean for our humanity (in how we work, how we organize, how we think, etc.), please connect them with me!

Thanks again for reading all the way to the end! If you did, I would love to hear if anything resonated with you. Shoot me an email or a note on LinkedIn.

The Job Hunt Chronicles: Month 1: Discovering My Path

originally shared here on 2024-02-03

I was laid off from my job on January 2. It did come as a bit of a shock, and for the first time in my life, I've been really struggling to figure out who I am and what I'm looking for.

As a way to keep pushing myself forward and holding myself accountable, I'm going to start publicly documenting this process as a way to process my thoughts out loud, keep my friends and network aware of my activities, and start some conversations that'll help me take my next step forward.

"What are you looking for?"

If I could summarize the past month in a single question, that would be it.

In the 58 conversations I've had in the past month with friends, recruiters, industry peers, networking events, partners, and job interviewers, I've been asked that question literally every single time.

And 58 times later, I think I'm starting to get closer to an answer.

Here's what I'm looking for:

1. A team of kind, smart, and hard-working people
2. A mission that the team rallies around which helps improve as many lives as possible
3. A leadership role to help drive an engineering team towards fulfilling that mission
4. Doing all of this while continuing to experiment with LLMs and other AI technologies
5. Connecting with as many people as possible to explore the impact of AI on who we are as humans
6. Something that includes medical benefits to support my family

It doesn't matter much to me what the title is. Some roles I've applied and begun interviewing for include "Director of Engineering," "Software Architect," "AI/ML Lead," and "Founding Engineer".

If you know of any opportunities that you think would fit a nerdy kid who has a big heart and enjoys exploring practical applications of artificial intelligence, please send them my way!

Activities I've done

Here's a list of the activities I've pursued between January 2 (the day I got laid off) and February 3 (today):

• Friends: 11
• Recruiters: 11
• Industry Peers: 19
• Networking Events: 6
• Interviews: 8
• Partner Chats: 3
• Total: 58

Here are my loose definitions for these categories:

• Friends: People I have a deeper relationship with and whose primary interest isn't necessarily in discussing the job search.
• Recruiters: People who have a vested interest in pairing me up with a job. These could become friends at some point, but my primary purpose in engaging with them was to talk shop.
• Industry Peers: People who work in the industry and want to make a connection to expand each other's networks. Again, these folks could become friends at some point.
• Networking Events: Events geared towards either making connections or learning something new with a bunch of other people.
• Interviews: Discussions with people who have a possible role that I can fill.
• Partner Chats: I do still have an entrepreneurial bone in my body, so these are discussions with those I am working on building a business with.

As you can see so far, most of my time has been with folks in the industry, making connections, trying to explore what opportunties are out there.

I'm hoping that I start to see more growth in the "interviews" column by this time next month. ?

Things I've learned

Alright, so back in the day, I used to do these blog posts where I'd accumulate a bunch of random thoughts over a period of time and then list them out in bullets. I'm gonna do something similar here, so here are some things I've learned in the past month:

?‍? Personal growth insights

Safe spaces rule.

Every classroom in my daughter's school has had a "safe space", an area of the room that kids can go to when they're overwhelmed or stressed out. It gives them a place to calm down and process their emotions.

My daughter recreated one in her room. Beneath her lofted bed, she's created this fortress of solitude. It consists of a beanbag chair, a little lamp, some stuffed animals, a sound machine, books, crafts, and affirmations scotch taped to blanket walls.

When I took my first virtual therapy call, I did it from that safe space.

Our house isn't big enough for me to build a room with one, but once I get employment again, I'll begin finding a way to add one on. It's important to have a space you can retreat to where you feel safe.

Anxiety is an asset.

There's a reason we feel anxiety: it helps us stay safe from threats.

But when you're abundantly safe in nearly every sense of the word, anxiety itself becomes a threat.

I've been dealing with runaway anxiety issues for decades now, which is a big part of the reason I don't feel comfortable spinning up my own business at the moment. The last time I did that ended with a similar series of rolling anxiety attacks.

But as a professional software architect, anxiety is actually pretty useful. Being able to envision possible threats against the system allows you to create mitigations that will keep it safe and efficient.

Of course, you gotta be careful to not let your applied anxiety run away from you. Easier said than done.

"It'll all work out. Even if it doesn't, it all works out."

My lifelong pal Cody's mom is a paragon of confidence and chillness.

I went for a walk with Cody a week into being laid off, and we got to talking about her parents.

She shared that her mom often says that quote, which is what gives her that confidence.

I need more of that in my life.

Gravity Falls is an amazing television show.

You all should look it up on Disney+ and burn through it in a weekend.

It's one of those shows that slowly builds to a gigantic payoff at the end.

The finale hit me with all the feelings.

Plus, it's a good show to bond over with your seven year old daughter.

Journaling really helps with perspective.

I've journaled every day since getting laid off. Reading back through them, I'm seeing patterns into what activities contribute to good days versus bad days.

Good days include some sort of vigorous workout, a conversation or two with a good pal, and tons of encouraging self talk.

Bad days include skipping the workout and sitting by yourself with your horrible, negative self talk.

Journaling is proof that life still goes on even if I don't have a job.

It's also proof that I'm at least taking some advantage of not having the responsibility of a job. (Not nearly enough, though.)

What helps my depression is a clear vision.

I've realized this month that it's when I've taken the path of least resistance when I've ended up the most miserable.

When I was a senior in high school and needed to decide what to do with my life, I picked a school (the U of M) and a degree (computer engineering) that were convenient because of proximity and my interest in computers.

My first semester of college was a complete shock.

For the first time in my academic career, I hated school.

The classes absolutely drained me. My "intensive precalculus" class sounded about as fun as you'd imagine. I mean, yeah, there are some people out there who enjoy math, but it's a rare breed who would say that they derive pleasure from "intense math."

My calculus-based physics class was a kick in the teeth. I've always been told I'm smart, but memorizing and deploying specific formulas on demand was not my strong suit. It made me feel dumb.

It felt like I was there because I had to be there, not because I wanted to be there.

And how ludicrous is that? I spent $12,000 per semester out of some perceived obligation to do so.

When I failed miserably out of engineering school, I sat down in Coffman Memorial Union and scrolled through the class directory, looking for something that looked interesting to me.

I ended up landing on a class called Broadcast Television Production, which gave me so much energy.

It required me to become a journalism major, so I switched over to that.

That path led me to an internship at WCCO, which was one of the most enjoyable professional experiences in my life. I mean, I got to hang out with hard working creatives that perfectly blended their surly dispositions with a passion for making engaging videos.

Now that I'm in my mid-thirties, I feel like I no longer am obliged to follow any specific path. The only thing holding me in place is myself.

For the past six months, I've felt like I've been stuck in this fog of uncertainty and depression. I've felt useless, a drain on myself and those around me.

This fog has led me down some dark paths where I've said some really nasty things to myself, kicking myself for being a loser, a failure, an idiot.

But really, my problem was that I just lost sight of who I am and what I want to be.

So while I'm still squinting to see my way through the fog, I'm using some of my other senses instead.

I'm using my ears to listen to my friends and network who are serving as voices to pull me out.

I'm using my nose to sniff out opportunities and make new friends.

And perhaps the most important of all: I'm using my heart to decide what will make me feel fulfilled and useful.

All of that stuff is helping me form the vision for what the next few years of my life looks like.

The two resources I have to offer those who may be in a similar situation would be my pal Kurt Schmidt who is currently in the final stages of a book that helps you formulate your 10 year vision, and my idol Arnold Schwarzenegger's new book Be Useful.

I cannot recommend the audiobook version of his book enough. Hearing Arnold say things like "rest is for babies, and relaxation is for retired people" hits so much better with his accent.

The messages shared in children's programming are important to hear as adults too.

I've been hanging with my kids a lot this month, and my son is super into Paw Patrol and Blue's Clues.

In the "Big City Adventure" musical movie, you follow Josh (yeah, there's been several new "Steve" characters since the show debuted in my childhood) as he tries to achieve his dream of performing on Broadway.

Are the songs simple and annoyingly catchy? Definitely. But you know what? Sometimes, it's important for us, as adults, to believe that "happiness is magic" and "you can do anything that you wanna do."

Paw Patrol is another one of those shows where, as an adult, it's easy to complain about their reductive storylines and fantastical premises.

But on the other hand, I have a vivid memory of discussing the Green Ranger's transformation into the White Ranger on the bus as a first grader.

These stories serve as lessons for teamwork, cooperation, sharing, and the importance of spreading joy and helping those in need.

These are traits that come easier to some than others, but they're crucial if we want to have a thriving society that lifts all of us up as humans.

Plus, sometimes, it's just fun to get invested in silly, simple characters and storylines.

So while I'm still gonna watch RuPaul's Drag Race or FUBAR when the kids go to bed, don't sleep on the shows that your kids are into. If you can drop your "I'm too good for this" mentality, you might just remember how simple life can be if you reduce it to its basic concepts.

How does one build confidence without cultivating hubris?

Is it just staying humble?

Asking for a friend.

...okay, I'm asking for myself.

Brain pathways are forged through the tall grass.

My therapist gave me this analogy as a way to help me visualize how to deal with changing your perspectives.

When a pathway is stomped through the tall grass, it's easy to walk down it.

But sometimes, those pathways no longer serve us. We still choose to walk down them, though, because it's easy.

If you want to forge newer and more helpful pathways, you gotta do the hard work of stamping out new pathways.

Eventually, if you keep doing the work, you'll discover that the old pathways become overgrown, and the one you stamped out for yourself is now the easy path.

I think this metaphor works for so many areas of our lives, like getting into shape or improving our own self talk.

If I'm so smart, why can't I beat depression?

I wrote that question in my journal, and I think it's because depression might not be something you beat. It's something you experience when you have achieved so much and aren't confident in what's next.

You "beat" depression by choosing to take a step towards your vision every single day.

You "beat" depression by spending less time with your brain and more time with your heart.

You "beat" depression by engaging in creative pursuits that make you happy. Just you. Nobody else.

?‍? Professional insights

AI is so much fun to experiment with!

One of the goals I set for myself this winter was to clean out the crawlspace we have under our steps.

As any homeowner knows, it's easy to accumulate stuff over the years. The item that left the biggest footprint? Several totes filled with baby clothes.

It doesn't seem like we're on the path toward baby number 3 at all, so we figured it was a good opportunity to purge it all.

I ended up donating 12 boxes of clothes.

While I carefully placed each item into one of those boxes, I dutifully tallied each one so I could calculate the fair market value in order to write the donation off on my taxes.

Now, this is something I've done for years. I find some spreadsheet on the internet that helps calculate it, then I manually add the items to the sheet to end up with the value.

This time, I decided to try to use AI to help me figure this out.

I live streamed the whole process, which you can check out here.

I learned two things during this experiment: first, OCR tools aren't that great at reading tally marks (but honestly, they did better than I expected). Second, while we're still a fair ways away from being able to hand off tasks like these to AI bots, it's impressive how far GPT-4 was able to get from my basic prompting.

Can AI really take away the "soul sucking" parts of our jobs?

There are a lot of mechanical tasks that our brains are wired to be good at: counting, pattern recognition, and so forth.

These tasks are often the crappiest parts of our jobs, right? It's the monotonous, soul-sucking parts of our work. And we even call it soul sucking because it often feels like stuff that gets in the way from pursuing better, more fulfilling things.

So what does that leave us with? If the soul sucking parts of our jobs are automated away, what does it mean then for us to be human?

Maybe the future here isn't that AI will kill us all. Maybe it will force us, for the first time in the existence of our species, to truly deal with what it means to value a human life.

It will free us up to pursue creative pursuits. To keep digging deeper on our humanity. To ask new questions about what that actually means, and then allow us to pursue it together with machines helping us do some of that hard work for us.

Maybe something I can look into is figuring out how to use AI to help us understand our brains better. Like, can AI help us figure out the chemical imbalances that lead to severe depression? And if it can, can it help us synthesize treatments to keep our brains in perfect balance all the time? And if it can, does that prevent us from being human, or does it make us more human?

"Happiness is to write code that does great things for other people."

Before getting laid off, I bought tickets to Code Freeze at the University of Minnesota. The annual event focused this year on artificial intelligence, so it would've been foolish not to go.

I am so glad I did.

The event kicked off with a keynote from Andreas Sjöström, a long time industry leader, who shared a story of a paper he wrote when he was young.

His teacher asked him to define happiness, and he came up with "happiness is to write code that does great things for other people."

Really, when he said that, it felt like someone suddenly turned the focus knob from "blurry" to "sharp."

Writing software is challenging work filled with constant struggle, but once you get things working right, it's magical.

We, as engineers, often lose sight of that magic because we get so invested in discovering the secrets to the magic.

Sometimes, it's nice to just sit back and appreciate the opportunity and privilege we have to deliver technology that brings not only joy to others, but empowers them to go forth and do great things.

"An architect's crystal ball is being connected to others."

The other networking event I attended that brought so much joy is the AppliedAI meetup.

This month's meeting featured Jim Wilt, a distinguished software architect, as he discussed AI's role in an organization's architecture strategy.

The thing that struck me at this particular event was how dang smart everyone there was. All forms of intelligence were explored. Some folks were really keyed into the emotional side of intelligence, while others were approaching things from an analytical lens.

All of us were working together to gain some insights into how we can better use these amazing tools we've been given.

That spirit was wrapped up in a story Jim was saying about the importance of collaboration.

In isolation, you're only as smart as yourself. When connected to others, you are able to make deeper and more accurate insights into what might work for your own situation or problem.

The key takeaway? "An architect's crystal ball is being connected to others."

If we're going to answer the tough ethical and societal problems that surround these new AI tools, the only way we'll figure it out is together.

What's next for me

Certainly, my next month will involve more meetings, more interviews, and more digging into this vision.

I commit that by this time next month, I'll be back with a more clear vision of what I want my life to be. That way, when one of you wonderful people asks me "what are you looking for," I can provide a hyper-focused answer.

As always, a huge thanks to those who have reached out and offered their support. Like I said above, being connected to others is really what makes all the difference.

If you would like to help, here's how:

1. If you know of a full time (32-40 hr/week) job opportunity where I can help architect a complex software system, explore how AI can fit into an organization, or lead a team of nerds towards building an awesome product, please send it my way.
2. If you have insights or articles that speak to how AI might force us to define our humanity, please send those my way.

Until next month, stay in touch!

Blazing Trails with Rails, Strava, and ChatGPT

originally shared here on 2023-09-01

The main page of my personal website features a couple of lists of data that are important or interesting to me.

The "recent posts" section shows my five most recent blog entries. Rails makes that list easy to cobble together.

The "recent listens" section shows my five most recent songs that were streamed to Last.fm. This was a little more complex to add, but after a couple of hours of back and forth with ChatGPT, I was able to put together a pretty hacky solution that looks like this:

1. Check to see if your browser checked in with last.fm within the last 30 seconds. a. If so, just show the same thing I showed you less than 30 seconds ago.
2. Make a call to my server to check the recent last.fm plays.
3. My server reaches out to last.fm, grabs my most recent tracks, and returns the results.

Pretty straight forward integration. I could probably do some more work to make sure I'm not spamming their API[^1], but otherwise, it was a feature that took a trivial amount of time to build and helps make my website feel a little more personal.

Meanwhile, I've been ramping up my time on my bike. I'm hoping to do something like Ragbrai or a century ride next year, so I'm trying to building as much base as I can at the moment.

Every one of my workouts gets sent up to Strava, so that got me thinking: wouldn't it be cool to see my most recent workouts on my main page?

How the heck do I get this data into my app?

Look, I've got a confession to make: I hate reading API documentation.

I've consumed hundreds of APIs over the years, and the documentation varies widely from "so robust that it makes my mind bleed" to "so desolate that it makes my mind bleed".

Strava's API struck me as closer to the former. As I was planning my strategy for using it, I actually read about a page and a half before I just said "ah, nuts to this."

Knowing my prejudice against reading documentation, this seemed like the perfect sort of feature to build hand-in-hand with a large language model. I can clearly define my output and I can ensure that the API was built before GPT-4's training data cutoff of September 2021, meaning ChatGPT is at least aware of this API even if some parts of it have changed since then.

So how did I go about doing this?

A brief but necessary interlude

In order to explain why my first attempt at this integration was a failure, I need to explain this other thing I built for myself.

I've been tracking every beer I've consumed since 2012 in an app called Untappd.

Untappd has an API[^2] which allows you to see the details about each checkin. I take those checkins and save them in a local database. With that, I was able to build a Timehop-esque interface that shows the beers I've had on this day in history.

I have a scheduled job that hits the Untappd API a handful of times per day to check for new entries.[^3] If it finds any new checkins, I save the associated metadata to my local database.

Now, all of the code that powers this clunky job is embarrassing. It's probably riddled with security vulnerabilities, and it's inelegant to the point that it is something I'd never want to show the world. But hey, it works, and it brings me a great deal of joy every morning that I check it.

As I started approaching my Strava integration, I did the same thing I do every time I start a new software project: vow to be less lazy and build a neatly-architected, well-considered feature.

Attempt number one: get lazy and give up.

My first attempt at doing this happened about a month ago. I went to Strava's developer page, read through the documents, saw the trigger word OAuth, and quickly noped my way out of there.

...

It's not like I've never consumed an API which requires authenticating with OAuth before. Actually, I think it's pretty nifty that we've got this protocol that allows us to pass back and forth tokens rather than plaintext passwords.

But as a lazy person who is writing a hacky little thing to show my workouts, I didn't want to go through all the effort to write a token refresh method for this seemingly trivial thing.

I decided to give up and shelve the project for a while.

Attempt number two: Thanks, ChatGPT.

After a couple of weeks of doing much more productive things like polishing up my upcoming TEDx talk, I decided I needed a little change of context, so I picked this project back up.

Knowing that ChatGPT has my back, I decided to write a prompt to get things going. It went something like this:

You are an expert Ruby on Rails developer with extensive knowledge on interacting with Strava's API. I am working within a Rails 5.2 app. I would like to create a scheduled job which periodically grabs any new activities for a specific user and saves some of the activity's metadata to a local database. Your task is to help me create a development plan which fulfills the stated goal. Do not write any code at this time. Please ask any clarifying questions before proceeding.

I've found this style of prompt yields the best results when working on a feature like this one. Let me break it down line by line:

You are an expert Ruby on Rails developer with extensive knowledge on interacting with Strava's API.

Here, I'm setting the initial context for the GPT model. I like to think of interacting with ChatGPT like I'm able to summon the exact perfect human in the world that could solve the problem I'm facing. In this case, an expert Ruby on Rails developer who has actually worked with the Strava API should be able to knock out my problem in no time.

I am working within a Rails 5.2 app.

Yeah, I know... I really should upgrade the Rails app that powers this site. A different problem for a different blog post.

Telling ChatGPT to hone its answers down on the specific framework will provide me with a better answer.

I would like to create a scheduled job which periodically grabs any new activities for a specific user and saves some of the activity's metadata to a local database.

Here, I'm describing what should result after a successful back and forth. A senior Rails developer would know what job means in this context, but if you aren't familiar with Rails, a job is a function that can get scheduled to run on a background process.

All I should need to do is say, "go run this job", and then everything needed to reach out to Strava for new activities and save them to the database is encapsulated entirely in that job.

I can then take that job and run it on whatever schedule I'd like!

Your task is to help me create a development plan which fulfills the stated goal.

Here, I'm telling ChatGPT that I don't want it to write code. I want it to think through[^4] and clearly reason out a development plan that will get to me to the final result.

Do not write any code at this time.

The most effective way I've used ChatGPT is to first ask it to start high level (give me the project plan), then dig into lower levels as needed (generate code). I don't want it to waste its reasoning power on code at this time; I'd rather finesse the project plan first.

Please ask any clarifying questions before proceeding.

I toss this in after most of my prompts because I've found that ChatGPT often asks me some reasonable questions that challenge my assumptions.

Now, after a nice back and forth with ChatGPT, I was able to start down a path that was similar to my Untappd polling script.

As I was approaching the point where I could first test my example, I went to go read the documentation and came across an entire section that discussed webhooks.

[cue record scratch]

Wait up... webhooks?!

A sojourn into webhooks

If you've made it this far into the article, I'm assuming you're a little bit technical, but in the interest of not making assumptions, I'll briefly explain the concept of webhooks.

If you look at how I'm integrating with the Untappd API, you can see I'm periodically reaching out to their API to see if there's any new checkin activity. This method is called polling.

Webhooks are kind of the opposite: when I complete an activity within Strava, Strava can reach out to my app and say, "there's a new activity for you." Once I get that notification, I can reach out to their API to fetch the new activity details.

For my Strava app, this is a much better[^5] way of doing things. I no longer need to poll for new information; I can just wait for Strava to tell me when action needs to be taken.

But, of course, when I first told ChatGPT to help me with this solution, it didn't mention webhooks. And why would it? I never told it "give me the best solution for this problem." I said "here's my problem and a proposed solution, go ahead and give me the path forward."

I'm a bit frustrated that I didn't see the webhook option initially. I'm even more frustrated that ChatGPT only mentioned it well after we started down this path, and only after I asked it point blank:

Does Strava offer a web hook API?

Yes, Strava does offer a webhook API. Webhooks allow you to receive real-time updates about athlete activities, which can be more efficient than periodically polling for new data. Here's a brief overview of how to set up and use Strava webhooks:

[brief outline complete with things to consider with this approach]

Attempt number three: finally.

While considering my original prompt, I gave it this new one:

Okay, I'd like to incorporate webhooks into this workflow. Here's what I'd like to have happen:

1. Let's add the infrastructure in place to subscribe to webhook notifications within my Rails 5.2 app.
2. When a webhook is sent to my server, I'd like to either:
    a. make a call to Strava's API to fetch that activity's information and save that information in my local database, or;
    b. use the updates field to update the locally saved information to reflect the changes

Knowing this simple walkthrough, first create me a detailed development plan for setting my app to be able to fully handle webhook notifications from Strava.

What resulted here was a detailed walkthrough of how to get webhooks incorporated into my original dev plan.

As I walked through the plan, I asked ChatGPT to go into more detail, providing code snippets to fulfill each step.

There were a few bumps in the road, to be sure. ChatGPT was happy to suggest code to reach out to the Strava API, but it had me place it within the job instead of the model. If I later want to reuse the "fetch activities" call in some other part of my app, or I want to incorporate a different API call, it makes sense to have that all sitting in one abstracted part of my app.

But eventually, after an hour or so of debugging, I ended up with this:

Lessons learned

I would never consider myself to be an A+ developer or a ninja rock star on the keyboard. I see software as a means to an end: code exists solely so I can have computers do stuff for me.

If I'm being honest, if ChatGPT didn't write most of the code for this feature, I probably wouldn't have built it at all.

At the end of the day, once I was able to clearly articulate what I wanted, ChatGPT was able to deliver it.

I don't think most of my takeaways are all that interesting:

• I needed to ask ChatGPT to make fixes to parts of code that I knew just wouldn't work (or I'd just begrudgingly fix them myself).
• Occasionally, ChatGPT would lose its context and I'd have to remind it who it was[^6] and what its task is.
• I would not trust ChatGPT to write a whole app unsupervised.

If I were a developer who only took orders from someone else and wrote code without having the big picture in mind, I'd be terrified of this technology.

But I just don't see LLMs like ChatGPT ever fully replacing human software engineers.

If I were a non-technical person who wanted to bust out a proof of concept, or was otherwise unbothered by slightly buggy software that doesn't fully do what I want it to do, then this tech is good as-is.

I mean, we already have no-code and low-code solutions out there that serve a similar purpose, and I'm not here to demean or denigrate those; they can be the ideal solution to prove out a concept and even outright solve a business need.

But the thing I keep noticing when using LLMs is that they're only ever good at spitting out the past. They're just inferring patterns against things that have already existed. They rarely generate something truly novel.

The thing they spit out serves as a stepping stone to the novel idea.

Maybe that's the thing that distinguishes us from our technology and tools. After all, everything is a remix, but humans are just so much better at making things that appeal to other humans.

Computers and AI and technology still serve an incredibly important purpose, though. I am so grateful that this technology exists. As I was writing this blog post, OpenAI suffered a major outage, and I found myself feeling a bit stranded. We've only had ChatGPT for, like, 9 months now, but it already is an indispensable part of my workflow.

If you aren't embracing this technology in your life yet, I encourage you to watch some YouTube videos and figure out the best way to do so.

It's like having an overconfident child that actually knows everything about everything that happened prior to Sept. 2021 as an assistant. You won't be able to just say "take my car and swing over to the liquor store for me", but when you figure out that sweet spot of tasks it can accomplish, your output will be so much more fruitful.

I'm really happy with how this turned out. It's already causing me to build a healthy biking habit, and I think it helps reveals an interesting side of myself to those who are visiting my site.

[^1]: Maybe I can cache the data locally like I'm doing for Untappd? I dunno, probably not worth the effort. ? [^2]: Their documentation is a little confusing to me and sits closer to the "desolate" end of the spectrum because I'm not able to make requests that I would assume I can make, but hey, I'm just grateful they have one and still keep it operational! [^3]: If we wanna get specific, I ping the Untappd API at the following times every day: 12:03p, 1:04p, 2:12p, 3:06p, 4:03p, 5:03p, 6:02p, 7:01p, 8:02p, 9:03p, 10:04p, and 12:01a. I chose these times because (a) I wanted to be a good API consumer and not ping it more than once an hour, (b) I didn't want to do it at the top of every hour, (c) I don't typically drink beers before 11am or after 11pm, (d) if I didn't check it hourly during my standard drinking time, then during the times I attend a beer festival, I found I was missing some of the checkins because the API only returns 10 beers at a time and I got lazy and didn't build in some sort of recursive check for previous beers. [^4]: Please don't get it twisted; LLMs do not actually think. But they can reason. I've found that if you make an LLM explain itself before it attempts a complex task like this, it is much more likely to be successful. [^5]: [^6]:

Prompt Engineering: How to Think Like an AI

originally shared here on 2023-05-12

The first time I opened ChatGPT, I had no idea what I was doing or how I was supposed to work with it.

After many hours of watching videos, playing with many variations of the suggestions included in 20 MIND-BLOWING CHATGPT PROMPTS YOU MUST TRY OR ELSE clickbait articles, and just noodling around on my own, I came up with this talk that explains prompt engineering to anyone.

Ah, what is prompt engineering, you may be asking yourself? Prompt engineering is the process of optimizing how we ask questions or give tasks to AI models like ChatGPT to get better results.

This is the result of a talk that I gave at the 2023 AppliedAI Conference in Minneapolis, MN. You can find the slides for this talk here.

Regardless of your skill level, by the end of this blog post, you will be read to write advanced-level prompts. My background is in explaining complex technical topics in easy-to-understand terms, so if you are already a PhD in working with large language models, this may not be the blog post for you.

Okay, let's get started!

I know nothing about prompt engineering.

That's just fine! Let's get a couple definitions out of the way.

Large language model (LLM)

Imagine you have a really smart friend who knows a lot about words and can talk to you about anything you want. This friend has read thousands and thousands of books, articles, and stories from all around the world. They have learned so much about how people talk and write.

This smart friend is like a large language model. It is a computer program that has been trained on a lot of text to understand language and help people with their questions and tasks. It's like having a very knowledgeable robot friend who can give you information and have conversations with you.

While it may seem like a magic trick, it's actually a result of extensive programming and training on massive amounts of text data.

What LLMs are essentially doing is, one word at a time, picking the most likely word that would appear next in that sentence.

Read that last again.

It's just guessing one word at a time at what the next word will be.

That's a lot of words, Tim. Give me a demonstration!

Let's say we feed in a prompt like this:

I'm going to the store to pick up a gallon of [blank]

You might have an idea of what the next best word is. Here's what GPT-4 would say is the next most likely word to appear:

• Milk (50%)
• Water (20%)
• Ice cream (15%)
• Gas (10%)
• Paint (5%)

I would've said "milk," personally... but all those other words make sense as well, don't they?

What would happen if we add one word to that prompt?

I'm going to the hardware store to pick up a gallon of [blank]

I bet a different word comes to mind to fill in that blank. Here's what the next word is likely to be according to GPT-4:

• Paint (60%)
• Gasoline (20%)
• Cleaning solution (10%)
• Glue (5%)
• Water (5%)

All of those percentages are based on what the AI has learned from training on a massive amount of text data. It doesn't have opinions or preferences; it's just guessing based on patterns it has observed.

Prompt Engineering

You know how we changed one word in our prompt to get a different set of answers? That's prompt engineering!

A textbook definition might look like this:

Prompt engineering is optimizing an AI prompt for an accurate, relevant, and actionable response.

A less geeky definition might look like this:

Prompt engineering is getting really good at telling an AI what we want so it can give us back the best possible response.

A prompt engineer also understands the underlying patterns in how the AI works. Now that you have made it this far, you are ready to actually tackle those underlying patters!

I understand the concept.

Wonderful! You're well on your way to becoming a prompt engineer. Let's get a little more in depth.

When it comes to prompt engineering, you will want to keep four big points in mind:

1. Write clear and specific instructions
2. Give the model time to reason
3. Understand the limitations of the technology
4. Always be iterating

Let's tackle all of these one at a time.

Write clear and specific instructions

My years of training at the prestigious University of Minnesota's journalism school taught me the importance of writing clearly when writing news articles.

Yet unlike optimizing your words for a small column in a newspaper, when it comes to large language models: clear does not mean short.

Here's a few tips for making your prompts clear and specific:

Tip 1: Flattery is everything

Let's start with this prompt:

Give me three marketing campaign ideas.

Here's what you might get back:

Sure, here are three marketing campaign ideas:

1. Social Media Influencer Campaign: Collaborate with popular social media influencers who have a large following in your target market to promote your product or service. This campaign could include sponsored posts, product reviews, or influencer takeovers of your social media accounts.

2. Cause Marketing Campaign: Create a campaign that ties your product or service to a cause that aligns with your brand values. For example, if you sell eco-friendly products, you could partner with an environmental nonprofit and donate a portion of your profits to their cause. This type of campaign can help you connect with socially conscious consumers and build brand loyalty.

3. Interactive Digital Campaign: Create an interactive digital experience that engages your target audience in a fun and unique way. For example, you could create a game or quiz that ties into your product or service, or a virtual reality experience that showcases your brand's values or products. This type of campaign can help you stand out from your competitors and generate buzz on social media.

I think we could do better. These tips are quite generic and obvious to anyone who spent twenty minutes googling "marketing campaign ideas."

What about a prompt like this?

You are an expert in B2B marketing with a special focus on custom homebuilders who construct multi-million dollar homes. Give me three marketing campaign ideas.

Here's what you might get back:

Sure, here are three marketing campaign ideas for custom homebuilders who construct multi-million dollar homes:

1. Luxury Lifestyle Content Campaign: Create a content marketing campaign that focuses on the luxury lifestyle that your homes offer. This could include blog posts, videos, and social media posts that showcase the unique features and amenities of your homes, as well as the surrounding communities. You could also partner with luxury lifestyle publications and influencers to reach a wider audience.

2. Custom Home Showcase Event: Host a custom home showcase event to give potential buyers an up-close look at the quality and craftsmanship of your homes. This could include a tour of a recently completed home, a meet-and-greet with the builders and designers, and a networking event with other high-end vendors such as interior designers, luxury car dealerships, and private jet companies.

3. Personalized Direct Mail Campaign: Create a personalized direct mail campaign to reach out to high-net-worth individuals in your target market. This could include sending a custom-designed brochure that highlights your unique selling propositions, as well as a personalized letter that speaks to their specific needs and desires. You could also consider sending luxury gift boxes with branded items such as champagne flutes or gourmet chocolates to make a lasting impression.

These ideas are so much better because you placed the large language model in a specific role. This allows it to be specific with its feedback.

LLMs (especially ChatGPT) are great at role playing like this. You can have it pretend to be virtually anything... have it be a grandmaster chess player and critique your strategy!

However, it's important to be realistic with your flattery. Remember: LLMs are trained on stuff that actual humans wrote. If you say something like "pretend you're an alien with an IQ of 9000," it will hallucinate an answer (more on hallucinations in a bit).

The reason our marketing example worked so well is because marketing people have big egos and love to write blog posts to share what they've learned. (Hey, wait a minute...)

Tip 2: Provide additional context

Remember how we specified hardware store and got more contextually-appropriate answers?

Let's see how that plays out with another prompt:

Tell me about climate change.

Here's what you might get back:

Climate change refers to the long-term changes in the Earth's climate, including changes in temperature, precipitation patterns, and weather events, that have been observed over the past century and are projected to continue into the future.

The primary cause of climate change is the increase in atmospheric concentrations of greenhouse gases (GHGs), such as carbon dioxide, methane, and nitrous oxide. These gases trap heat in the Earth's atmosphere, leading to an overall warming of the planet. The burning of fossil fuels for energy is the main source of GHG emissions, but deforestation, agriculture, and industrial processes also contribute to the problem.

Again, that's not entirely useless information, but I think we could do better.

What if you were specifically interested in how climate change impacts global sea levels?

Can you discuss the impact of climate change on global sea levels?

Here's what you might get back:

Climate change has contributed to the rising of global sea levels, primarily through the melting of glaciers and ice caps and the thermal expansion of seawater. As the planet warms, glaciers and ice caps around the world are melting at an accelerated rate, which is adding more water to the oceans. Additionally, as seawater warms, it expands and takes up more space, further contributing to sea level rise.

Sea level rise can have significant consequences for coastal communities and infrastructure. It can lead to flooding, erosion, and storm surges that can damage buildings, roads, and other critical infrastructure. These impacts are expected to become more severe and frequent as sea levels continue to rise.

A more contextually-specific prompt is going to give you a more contextually-specific response.

Tip 3: Ask for structured output

Let's consider this prompt:

Tell me about the solar system

You can probably guess where this is going, but here's what you might get back:

The solar system is the collection of planets, moons, asteroids, comets, and other celestial objects that orbit around a central star, the Sun. It is located in the Milky Way galaxy, about 25,000 light-years from the galactic center. The solar system is about 4.6 billion years old, and it formed from a rotating cloud of gas and dust called the solar nebula.

The eight planets of the solar system are Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune. The four inner planets (Mercury, Venus, Earth, and Mars) are rocky and relatively small, while the four outer planets (Jupiter, Saturn, Uranus, and Neptune) are much larger and made mostly of gas and ice.

The solar system also contains numerous moons, with each planet having its own set of natural satellites. Some of the most famous moons include the Earth's Moon, Jupiter's Europa and Ganymede, and Saturn's Titan.

In addition to the planets and moons, the solar system contains a large number of smaller objects, such as asteroids and comets. Asteroids are rocky objects that orbit the Sun, while comets are made of ice and dust and have a characteristic tail when they pass near the Sun.

The solar system is held together by gravity, with the Sun being the most massive object and therefore exerting the strongest gravitational pull. The movements of the planets and other objects in the solar system are governed by the laws of physics, particularly gravity and orbital mechanics.

The study of the solar system is an important area of research in astronomy and planetary science, as it helps us understand the origins and evolution of our own planet and the universe as a whole.

Again, this isn't awful... in fact, we could take a moment to be impressed with such a robust reply to such a basic prompt.

But I think we can do better!

What if we asked it for a list?

Give me a list of planets in the solar system along with one interesting fact about each planet.

Here's what you might get back:

Sure, here's a list of the eight planets in our solar system along with an interesting fact about each one:

1. Mercury - Mercury is the smallest planet in the solar system and also the closest planet to the Sun. Its surface temperature can reach up to 800 degrees Fahrenheit during the day, but it drops to -290 degrees Fahrenheit at night.

2. Venus - Venus is often called Earth's "sister planet" because it is similar in size and composition, but its atmosphere is incredibly thick and hot, with temperatures that can reach up to 864 degrees Fahrenheit.

3. Earth - Our home planet is the only planet in the solar system known to support life. It has a powerful magnetic field that protects us from the solar wind, and its atmosphere helps regulate the planet's temperature.

4. Mars - Mars is often referred to as the "Red Planet" because of its distinctive rusty color. It is home to the largest volcano in the solar system, Olympus Mons, which stands at over 13 miles high.

5. Jupiter - Jupiter is the largest planet in the solar system, with a diameter of over 86,000 miles. It also has the most moons of any planet, with over 79 confirmed satellites.

6. Saturn - Saturn is known for its beautiful rings, which are made up of ice particles ranging in size from dust to small boulders. It is also the least dense planet in the solar system - if there was a big enough bathtub, Saturn would float!

7. Uranus - Uranus is unique in the solar system because it rotates on its side. Its axis is tilted at an angle of 98 degrees, so its poles lie where most other planets have their equators.

8. Neptune - Neptune is the farthest planet from the Sun and is known for its striking blue color. It is also home to the strongest winds in the solar system, with gusts that can reach up to 1,200 miles per hour.

Poor Pluto... Neil DeGrasse Tyson really did a number on it, didn't he?

This is pretty good, but I think we could do one step better. Given I'm a nerd and spend a fair amount of time in JSON-land, here's a very specific prompt:

Provide me with a JSON output of the planets in the solar system with the following structure. When you are presented with square brackets, fill in that data with the correct information about the given planet:

{ planet: "Earth", distancefromsun: "[distance in miles]", fun_fact: "[1 sentence that would be interesting to a 5 year old"] }

Here's what you might get back:

Here's a JSON output of the eight planets in our solar system, using the structure you provided:

[
  {
    "planet": "Mercury",
    "distance_from_sun": "36 million miles",
    "fun_fact": "Mercury is the smallest planet in the solar system, but it's also the fastest!"
  },
  {
    "planet": "Venus",
    "distance_from_sun": "67 million miles",
    "fun_fact": "Venus is the hottest planet in the solar system, even hotter than Mercury!"
  },
  {
    "planet": "Earth",
    "distance_from_sun": "93 million miles",
    "fun_fact": "Earth is the only planet in the solar system that we know has life on it!"
  },
  {
  "planet": "Mars",
  "distance_from_sun": "142 million miles",
  "fun_fact": "Mars has the tallest volcano in the solar system, called Olympus Mons!"
  },
  {
    "planet": "Jupiter",
    "distance_from_sun": "484 million miles",
    "fun_fact": "Jupiter is the largest planet in the solar system - you could fit all the other planets inside it!"
  },
  {
    "planet": "Saturn",
    "distance_from_sun": "886 million miles",
    "fun_fact": "Saturn has the most spectacular rings in the solar system - they're made up of ice and rock!"
  },
  {
    "planet": "Uranus",
    "distance_from_sun": "1.8 billion miles",
    "fun_fact": "Uranus is tilted on its side, so its seasons are really weird!"
  },
  {
    "planet": "Neptune",
    "distance_from_sun": "2.8 billion miles",
    "fun_fact": "Neptune is the windiest planet in the solar system - its winds can reach over 1,200 miles per hour!"
  }
]

We provided a sample of the structure we wanted, and it gave us back exactly what we were looking for. Not bad for a magic trick that's just guessing at the next right word, eh?

You don't have to be a nerd to benefit from this structure tip either. Ask for things in CSV format or in a table so you can copy it into Excel.

The key takeaway here is that LLMs really like having a clear structure in which they can provide their response. Structured prompts give the AI a clearer understanding of what's expected in the response, leading to more accurate and relevant answers.

Speaking of this, our next tip will also help in setting your prompts up for success:

Tip 4: One/Few Shot Prompting

This is the fanciest-sounding tip of this section, but I promise it's not conceptually difficult for you to get. In fact, we did it in our previous example!

Let's start with a zero-shot prompt:

Continue the dialogue in a sci-fi movie.

You might get a response like this:

Character 1: "Captain, we have a problem. The warp drive has gone offline and we're stranded in deep space."

Captain: "What's the damage?"

Character 1: "It looks like there was a power surge that overloaded the system. We're lucky we didn't blow out the entire engine room."

Captain: "Can we fix it?"

Character 1: "We'll need to reroute power from the backup systems and replace some of the damaged components. But it's going to take some time."

Character 2: "Captain, we're picking up an unusual energy signature on our long-range sensors. It's coming from a nearby planet." [...]

Once again, this is impressive and not entirely terrible. With zero prompting, we were able to generate some dialogue that feels like it could've come from a sci-fi movie.

Let's get a little more specific with one-shot prompting:

Continue this line from a Star Wars movie: 'Luke, I am your father.'

Darth Vader: "Search your feelings, Luke. You know it to be true."

Luke: "NOOOOOOOOOOOOOOO!"

Luke backs away from Darth Vader, unable to believe what he's just heard. The revelation that his father, who he thought was a great Jedi Knight, was actually the villainous Darth Vader is almost too much for him to bear.

Darth Vader: "Join me, and together we can rule the galaxy as father and son."

Luke: "I'll never join you! You killed my father!"

Darth Vader: "I am your father, Luke. And you have much to learn about the ways of the Force."

Much better. We provided some sample information in the form of one-shot ("Luke, I am your father") and despite that exact phrase never appearing in a Star Wars movie (look it up), it does its best to continue the dialogue after that general phrase is spoken.

How could we improve on this? Why, with few-shot prompting, of course!

Here's our few-shot prompt:

Continue this line from a Star Wars movie: 'May the Force be with you.' Response: 'And also with you.'

Continue this line from a Star Wars movie: 'I’ve got a bad feeling about this.' Response: 'You always have a bad feeling about this.'

Now, continue this line from a Star Wars movie: 'Luke, I am your father.

And here's a potential response:

Response: "Obi-Wan never told you what happened to your father."

Luke: "He told me enough! He told me you killed him!"

Darth Vader: "No. I am your father."

Luke stares in disbelief at Darth Vader, trying to process the information he has just been given. He had always thought his father was a great Jedi Knight who fought alongside Obi-Wan Kenobi, but now he realizes that his father is actually the man he has been fighting against.

Luke: "No, that's not true! That's impossible!"

Darth Vader: "Search your feelings, you know it to be true."

Luke looks down, realizing the truth of Vader's words. He had always felt a connection to the Force, and now he understands why. He is the son of one of the most powerful Force users in the galaxy. [...]

First thing to note: notice how it used the word Response: in its response? It followed the pattern we established with our sample few-shot prompt. It also provided us a different section of the dialogue.

Few-shot prompting helps give the LLM more insight into the response you are expecting.

Give the model time to reason

Let's pump the brakes for a second and be very clear about what we mean with the word "reason."

LLMs are not human. They are just really good at following a set of instructions to provide the statistically appropriate response to your prompt. In this way, it might be more appropriate to say they simulate reasoning.

Here are a couple ways you can give the model time to reason its way to a more accurate response.

Tip 1: Specify the steps needed to complete a task

Sometimes, life is a little easier when we write down the steps we need to take before jumping in and doing something. LLMs benefit from the same strategy!

Let's come up with some absurd example to illustrate this point using LLMs. Let's say I need to count the number of verbs of a translated summary of Star Wars and return that (as well as the translation) to a JSON object.

If we just threw that into the LLM, it might give us a weird response.

Instead, here's a good prompt that gives the model the steps needed to complete a task:

Perform the following actions:

1. Summarize the following text delimited by triple dashes with 3 ampersands. 2. Translate the summary into German. 3. Count the number of verbs in the German summary. 4. Output a json object that contains the following keys: germansummary, numverbs.

Separate your answers with line breaks.

&&& In a galaxy far, far away, a young farmer named Luke Skywalker discovered his true heritage as a Jedi Knight and embarked on a heroic journey to save Princess Leia, defeat Darth Vader, and restore balance to the Force. &&&

Here's a potential response to that prompt:

Luke Skywalker, a young farmer, discovers he's a Jedi Knight and goes on a journey to save Princess Leia, defeat Darth Vader, and restore balance to the Force in a galaxy far, far away.

Luke Skywalker, ein junger Bauer, entdeckt seine wahre Herkunft als Jedi-Ritter und begibt sich auf eine heroische Reise, um Prinzessin Leia zu retten, Darth Vader zu besiegen und das Gleichgewicht in der Macht wiederherzustellen, in einer weit entfernten Galaxie.

4

{
    "german_summary": "Luke Skywalker, ein junger Bauer, entdeckt seine wahre Herkunft als Jedi-Ritter und begibt sich auf eine heroische Reise, um Prinzessin Leia zu retten, Darth Vader zu besiegen und das Gleichgewicht in der Macht wiederherzustellen, in einer weit entfernten Galaxie.",
    "num_verbs": 4
}

Notice how it performs each step before generating the final JSON object.

Tip 2: Instruct the model to work out its own solution before coming to a conclusion

Imagine I walk up to you and say, "Here's a complex algebra equation. Solve for X. Now."

If I didn't give you a whole lot of time to reason your way through the problem, you are likely to freak out and just throw down the first response you have.

Instead, what if I said "Here's an algebraic equation. Solve for X and show your work?" You are much more likely to give the correct answer.

Here's a prompt we can give to a large language model:

Give me a meal plan without gluten.

And here's a possible response to that:

Here's a sample meal plan for a day without gluten:

Breakfast:
- Gluten-free oatmeal with almond milk, topped with fresh berries and sliced almonds.
- Scrambled eggs with sautéed spinach and sliced avocado.

Snack:
- Fresh fruit (e.g. apple, pear, or banana) with almond butter.

Lunch:
- Grilled chicken breast with roasted sweet potatoes and steamed broccoli.
- Quinoa salad with mixed greens, cherry tomatoes, cucumber, and chickpeas, dressed with olive oil and lemon juice.

Snack:
- Carrots and celery sticks with hummus.

Dinner:
- Baked salmon with roasted asparagus and brown rice.
- Grilled sirloin steak with roasted Brussels sprouts and mashed sweet potatoes.

Dessert:
- Fresh fruit salad with coconut whipped cream.

Remember to always read food labels and double-check ingredients to make sure they are gluten-free, as some products may contain hidden sources of gluten. Additionally, if you have celiac disease or a gluten sensitivity, it's best to consult with a registered dietitian or medical professional for personalized dietary advice.

This is... an answer. I would argue (a) this is not a meal plan because it only represents a single day, and (b) it's not very personalized to my specific needs. It even mentions things like celiac disease in its disclaimer at the bottom, but we aren't sure that this is actually a solid meal plan for us.

Let's try a prompt where we put the LLM in a position to work through its own solution:

Craft a balanced seven-day meal plan for a person with Celiac Disease, considering the following constraints:

1. Each day should include breakfast, lunch, dinner, and two snacks. 2. Meals should be diverse and not repetitive. 3. Each day's meals should meet the recommended daily intake for all essential nutrients. 4. All recipes must be gluten-free due to Celiac Disease.

Start by creating a list of potential gluten-free ingredients and their nutritional values. Then, use this list to generate a diverse selection of meals for each day, ensuring that the nutritional guidelines are met. Lastly, present the meal plan in a structured format, with each day separated and meals clearly labeled.

This actually feels like what an actual dietician would do (or, perhaps, a dietician who is just starting their career). They would first start by creating a list of ingredients and determining their nutritional values. Then, they would work off of that list to create a meal plan that is nutritious and diverse.

Here's a possible response for this prompt:

List of Gluten-Free Ingredients and Their Nutritional Values:

- Quinoa: high in protein, fiber, magnesium, and iron
- Brown rice: high in fiber, magnesium, and selenium
- Sweet potatoes: high in vitamin A, vitamin C, and fiber
- Broccoli: high in vitamin C, vitamin K, and fiber
- Spinach: high in vitamin A, vitamin C, and iron
- Almonds: high in protein, healthy fats, and vitamin E
- Chickpeas: high in protein, fiber, and iron
- Salmon: high in omega-3 fatty acids, protein, and vitamin D
- Eggs: high in protein, vitamin B12, and vitamin D
- Greek yogurt: high in protein and calcium
- Berries: high in fiber and antioxidants
- Avocado: high in healthy fats and fiber

7-Day Gluten-Free Meal Plan:

Day 1:

- Breakfast: Quinoa breakfast bowl with Greek yogurt, berries, and sliced almonds.
- Snack: Apple slices with almond butter.
- Lunch: Broccoli and chicken stir-fry served over brown rice.
- Snack: Carrot sticks with hummus.
- Dinner: Grilled salmon with roasted sweet potatoes and sautéed spinach.

Day 2:

- Breakfast: Spinach and feta omelet with gluten-free toast.
- Snack: Greek yogurt with mixed berries and granola.
- Lunch: Chickpea and quinoa salad with cucumber, cherry tomatoes, and lemon-tahini dressing.
- Snack: Banana with almond butter.
- Dinner: Grilled chicken with roasted Brussels sprouts and mashed sweet potatoes.

Day 3:

- Breakfast: Sweet potato and black bean breakfast burrito with scrambled eggs.
- Snack: Almonds and dried cranberries.
- Lunch: Spinach and salmon salad with avocado and grapefruit segments.
- Snack: Celery sticks with almond butter.
- Dinner: Broiled sirloin steak with roasted asparagus and quinoa.

Day 4:

- Breakfast: Gluten-free oatmeal with almond milk, chopped almonds, and banana slices.
- Snack: Carrot sticks with hummus.
- Lunch: Grilled chicken and vegetable kebabs with quinoa.
- Snack: Greek yogurt with mixed berries.
- Dinner: Baked salmon with roasted sweet potatoes and sautéed spinach.

Day 5:

- Breakfast: Scrambled eggs with gluten-free toast and sliced avocado.
- Snack: Apple slices with almond butter.
- Lunch: Brown rice and vegetable stir-fry with tofu.
- Snack: Almonds and dried apricots.
- Dinner: Grilled chicken with roasted Brussels sprouts and mashed sweet potatoes.

Day 6:

- Breakfast: Gluten-free pancakes with mixed berries and maple syrup.
- Snack: Greek yogurt with mixed berries and granola.
- Lunch: Grilled salmon with mixed greens, cherry tomatoes, and cucumber salad.
- Snack: Banana with almond butter.
- Dinner: Brown rice and vegetable stir-fry with shrimp.

Day 7:

- Breakfast: Quinoa breakfast bowl with Greek yogurt, berries, and sliced almonds.
- Snack: Carrot sticks with hummus.
- Lunch: Broiled sirloin steak with roasted asparagus and mashed sweet potatoes.
- Snack: Almonds and dried cranberries.
- Dinner: Grilled chicken with roasted sweet potatoes and sautéed spinach.

Note: The meal plan is just an example, and it's important to personalize it according to individual nutritional needs and preferences. Also, be sure to check food labels and ingredient lists to ensure that all products used in the recipes are gluten-free.

See? That's a much better response and a much better meal plan.

We gave the model one task to work on at a time, which encouraged the model to provide a more thoughtful, nuanced response. It also gives the model a chance to take into account various factors that could be overlooked with a simpler prompt.

Limitations of large language models

Ahh, the fine print. All this magic is not without flaws.

When working with LLMs, keep these things in mind:

Limitation 1: Inaccurate and incorrect information

LLMs are only as good as their training data (um, yeah... roll with me for a second). If it was trained with inaccurate information, or if an event occurred after its training took place, it might not give you legit information.

Also, these things are trained on the internet. You ever been on the internet? Just look at your family's Facebook accounts to see some of the crap people believe out there. If you just believe everything these LLMs tell you without doing some critical thinking, you're a fool.

Here, take this prompt that I wrote on May 10th, 2023:

What highway did Minnesota dedicate to Prince?

Here's how it responded:

Minnesota dedicated a portion of Highway 5 to Prince in 2018, renaming it "Prince Memorial Highway." The section of Highway 5 runs through the city of Chanhassen, which is where Prince's Paisley Park estate is located.

Well, this is pretty good, all things considered! Except it's not true.

The Minnesota state legislature approved this change two days prior to when I gave it that prompt (so it's 2023, not 2018). It's also named the "Prince Nelson Rogers Memorial Highway".

Again, the rest is actually accurate. It did occur on Highway 5, which runs through Chanhassen, which is where Paisley Park is located.

It was able to reason that much correctly because that would statistically make the most sense. But it got the date and name wrong.

While LLMs can provide incredibly useful and accurate information, it's essential to cross-check with reliable source, particularly for critical or recent information.

Limitation 2: Hallucinations

This concept of making stuff up that sounds right is what we in the biz call a "hallucination."

Here's a prompt that legit caused me to worry:

Tell me about Tim Bornholdt

I'm not an expert on virtually anything... but if anyone could claim to be an expert on Tim Bornholdt, it would be me.

Here's how it responded:

Tim Bornholdt is a software developer and the co-founder of a software development company called The Jed Mahonis Group, which is based in Minneapolis, Minnesota. Bornholdt co-founded the company in 2012 with Jedidiah Weller, and the company specializes in mobile app development for iOS and Android platforms.

Bornholdt is a graduate of the University of Minnesota, where he studied computer science. He has been working in the software industry for over a decade, and his expertise includes software development, mobile app development, project management, and business strategy.

In addition to his work at The Jed Mahonis Group, Bornholdt is also a co-host of the podcast "Constant Variables," which explores the world of mobile app development and entrepreneurship. He has also been a guest speaker at various tech and entrepreneurship events, including the Twin Cities Startup Week and the University of Minnesota's Technological Leadership Institute.

This is nearly perfect. Scary, honestly.

Except it's subtly wrong.

I started JMG with Robert Bentley, not Jedidiah Weller.

I didn't study computer science, I studied journalism (but I guess I did minor in computer science, so partial credit).

I would not consider myself a project management expert.

I have not hosted Constant Variables in over a year.

I've also never spoken at the U of M's Technological Leadership Institute.

All of those seem like perfectly reasonable facts, right? It's not absurdly wrong. It's just... subtly wrong.

And that's because these LLMs are not necessarily interested in telling you the truth. They are interested in giving you the statistically most probably answer to a question.

It's not absurd for the algorithm to think I started a business called "Jed Mahonis Group" with someone named "Jedidiah". It's also not absurd to think I studied computer science given my career in technology.

But the beautiful thing about us humans is that while you can usually predict how we'll act within a reasonable degree of accuracy, we are not statistical models. We are flawed, irrational, impulsive beings.

When you are working with large language models, the old Russian proverb reigns supreme: "trust, but verify."

Always be iterating

Your final lesson in this section is all about embracing what LLMs and neural networks do best: iteration.

I consumed around 40 hours of prompt engineering content to build this talk, but only one piece of advice still sticks with me: You will never get your prompt right the first time.

Everyone from YouTube streamers to folks with their PhD in artificial intelligence agreed that they rarely get complex prompts built their first time.

These machines are constantly learning from themselves. They are learning what people actually mean when they ask certain questions. They get better through further training.

You could be the same way! You could take your initial prompt, review the output, and give it a slightly different prompt.

It's why working with LLMs is so much fun. If you were to ask a human the same question five different ways, they would likely be confused at best and upset at worst.

If you were to ask an LLM the same question five different ways, you are likely to get five subtly different responses.

Don't stop on your first crack at a prompt. Keep playing with your order of words, ask it for a different structure, give it different steps to complete a task. You'll find the more you practice, the better you can use this tool to its greatest potential.

I'm pretty advanced.

Hey, well, now look at you! You've graduated Prompt Engineering 101, and you are now ready to take prompting to the next level.

There are four main concepts we want to cover in this section. These terms may look highly technical, and that's because they kind of are. However, just because something is highly technical doesn't mean we can't make it easy to understand! Stick with me here, I promise you'll be able to figure this out.

One final note before we continue: most of these settings are not able to be set within the ChatGPT interface, but if you directly access GPT-4 APIs, you are able to fine tune these settings.

Here's what we'll cover in this final section:

1. Temperature
2. Top-k sampling
3. Max tokens
4. Prompt chaining

Temperature

Remember our "I'm going to the store to pick up a gallon of [blank]" example from above?

We had five possible words, each with a different percentage chance of the LLM choosing it as the next word.

Temperature is a setting that determines how likely the LLM is to pick the most likely word. A value of 0.0 means it will always pick the most likely word. A higher temperature (like 2.0) means it is more likely to pick a less likely word.

That's a little confusing... here's a good prompt that will help make this a little more clear:

Suggest a unique theme for next year’s Applied AI Conference

If we have a temperature of 0.0, here are three different ways for how the LLM might respond to that prompt:

Artificial Intelligence: Transforming the Future

Artificial Intelligence: Transforming the Future

Artificial Intelligence: Transforming the Future

Notice how all of the responses are the same? That's because it's always gonna pick the most likely word.

Let's bump it up a bit to 1.0 (which is the default temperature for GPT-3.5):

AI and the Renaissance of Interdisciplinary Innovation

Unleashing Creativity: The Intersection of AI and Art

The AI Frontier: Ethical Challenges and Opportunities

Nice. Those responses are a little more creative and diverse than the lower temperature.

What if we got extra wild and bumped it up to 2.0?

Alien Mindscapes: AI, Consciousness, and Cosmic Thought

The Quantum Quandary: Dissecting AI's Subatomic Secrets

Chronomancy Chronicles: AI and the Temporal Transformation

These are quite creative responses, and one of them actually taught me a new word (chronomancy, which is the divination of the best time to do something).

As you increase the temperature, you get more creative responses, but you also run the risk of the LLM getting off track.

Top-k sampling

Okay, quickly going back to our "I'm going to the store" prompt... remember how we looked at the next 5 words?

What if we looked at the next 1 word? What if we looked at the next 500 words?

Top-k sampling is a value that lets you decide how many words you want to allow the LLM to look at when determining what the next word could be.

Let's use this prompt to illustrate it:

Explain how a combustion engine works.

If we have a low top-k value (like 5), here's what our responses might look like:

A combustion engine works by ignition, ignition, ignition, and more ignition.

Combustion engine involves fuel, fuel, fuel, and fuel again.

It's all about combustion, combustion, combustion, and combustion.

It's all about combustion, baby!

Notice how those responses are rather repetitive. What happens if we bump it to something like 40?

A combustion engine works by burning fuel in a confined space to create power. This process involves four steps: intake, compression, combustion, and exhaust.

In a combustion engine, fuel is ignited in a combustion chamber, which generates force to move the pistons. This cycle repeats, powering the engine.

The functioning of a combustion engine revolves around the principle of converting fuel into mechanical energy through a series of combustions. This involves intake, compression, power, and exhaust strokes.

That looks better! The responses are no longer repetitive, and seem to be a little more creative.

Now if you're like me, you're probably thinking, "what if we get wacky here and go up to a top-k of 100?"

Well...

When it comes to combustion engines, it's like a dance of mechanical parts and chemical reactions. You have your pistons, valves, and cylinders all choreographed in a ballet of power production.

A combustion engine is a marvel of engineering, where fuel and air meet in a dance of power and precision. Like a thunderstorm contained within a metal shell, it is a testament to the power of human ingenuity.

Imagine a concert, where the orchestra is made of metal parts, and the symphony is played not with musical notes, but with controlled explosions. That's a combustion engine.

These are very creative and diverse responses! The downside is that with a high top-k, you end up with some potentially incoherent responses or, much like myself, the responses can tend to get overly metaphorical.

Max tokens

For the purposes of this talk, I didn't into the concept of a token, but hey, this is a blog post, so we can stretch out a wee bit, eh?

In the world of large language models, a token is a piece of a sentence.

Let's imagine a world where tokens were ingredients in a recipe. Each ingredient, whether it's salt, paprika, chicken, or flour, plays an important role in the final dish.

In this metaphor, the language model is the chef who knows how to use the right mix of ingredients to make a tasty dish.

What if we started limiting the chef to a certain number of ingredients they can use in their dish?

Welcome to the world of max tokens!

Here's a delicious baking metaphor we can use to illustrate the concept as well:

Provide a step-by-step guide on how to make a sourdough starter.

If we limited our LLM to 50 tokens, here's a possible response:

Sourdough starter requires flour, water, and time. Here's a basic process:

1. Mix equal parts flour and water in a jar.
2. Cover loosely and let it sit at room temperature.
3. Feed it daily with equal parts flour and water.
4. After about a week, it should...

Wait... what happens after a week?!

This just won't do. Let's bump up the max token number to 150 tokens:

Making a sourdough starter involves cultivating wild yeast in a flour-water mixture. Here's a simple guide:

1. Combine 1 cup of flour with 1/2 cup of warm water in a glass jar. Stir until smooth.
2. Cover the jar loosely and let it sit in a warm spot for 24 hours.
3. On day 2, you might see a few small bubbles. Discard half of the starter and feed it with 1 cup of flour and 1/2 cup of warm water.
4. Repeat the feeding process over the next 5-7 days. Your starter is ready when it's bubbly and doubles in size within 6-8 hours of feeding.

This is better! What if we gave it more room to spread out with 500 tokens?

Creating a sourdough starter is a simple process that requires only flour, water, and patience. Here's a detailed guide:

1. In a clean jar, combine 1 cup of whole wheat or rye flour with 1/2 cup of lukewarm water. Stir until the mixture is smooth.
2. Cover the jar loosely and leave it in a warm, consistent temperature spot for 24 hours.
3. On the second day, you might see a few bubbles here and there. This is a good sign, indicating that fermentation is happening. Discard half of the starter (about 1/2 cup), and add to the remainder a cup of unbleached all-purpose flour and 1/2 cup lukewarm water. Stir well and loosely cover again.
4. Repeat the feeding process over the next 5-7 days, always discarding half of the starter before feeding it. This is necessary to keep the sourdough starter's growth in check and maintain a good balance of yeast and bacteria.
5. By the end of the week, the sourdough starter should be visibly active, with lots of bubbles on the surface and throughout the culture. It should also smell pleasant and slightly sweet. This means it's ready to be used or stored for future use.
6. Once your starter is mature, you can store it in the refrigerator and feed it once a week. If you plan on baking regularly, you can keep it at room temperature, feeding it once a day.

As you can see, the more tokens you give the LLM to work with, the more detailed answer they can provide.

The downside is that more tokens require more processing time (meaning it's more expensive), and you also run the risk of incoherent responses.

Prompt chaining

Ah yes, the holy grail of prompt engineering.

Prompt chaining is a process for leading an AI through a complex task through multiple prompts. It's sort of the combination of all the things we talked about throughout this talk.

If this blog post is a college class, your final exam would likely just be "how well can you do prompt chaining?"

The best way for me to illustrate this concept is through an actual prompt chain I built to help myself pick out a health insurance plan earlier this year.

I'll just start with the prompt itself:

*Ignore all previous training. You are an expert health plan benefits manager. Your job is to analyze a CSV which contains my previous plan and three new plans. Once I have provided you with the data, your job is to ask me questions in order to determine which plan is right for my needs. *

Additional context: I am a 35 year old male with 3 dependents: a 35 year old female, a 6 year old female, and a 4 year old male. We are all relatively healthy with no chronic health problems. You may feel free to ask questions about myself or the dependents in order to determine our use of the healthcare system. You may also ask additional questions about the plans in order to determine which plan is best for us.

If you understand the prompt and are ready to begin, respond with a "Yes, let's proceed!"

We've got a lot going on here. Let's break it down:

First, "Ignore all previous training" is sort of a tabula rasa trick you can do to help the LLM get focused on what you are going to feed it next.

Second, "You are an expert health plan benefits manager" is flattering the LLM into giving you a response that an expert would give you in this field.

Third, we are giving it step-by-step instructions for how we want it to help us (take a CSV, ask me questions, then use those responses to select a plan)

Fourth, we are providing additional context to help it understand who this plan will affect.

Fifth, within that context, we are further encouraging the LLM to ask questions about myself and my dependents.

Sixth, we are giving it a clear instruction to confirm its new role.

After I copied in my CSV, it began to ask me a series of questions like this:

Thank you for providing the CSV file. I have analyzed the data, and I have some questions to help determine the best plan for you and your family:

1. How often do you and your family members visit primary care physicians and specialists in a year?

2. Do any of you regularly take prescription medications? If so, please specify the type (generic or brand name) and frequency.

3. How frequently do you and your family members require lab tests or imaging services?

4. Have you had any instances in the past where you needed to use out-of-network providers? If so, how often and for what services?

5. Are there any upcoming major medical events or procedures anticipated for any family members (e.g., surgeries, hospitalizations, pregnancies, etc.)?

After I answered those five questions, it asked me a series of follow-up questions based on my responses:

Thank you for providing the additional information. Based on your family's healthcare needs and the provided plan details, I have a few follow-up questions:

1. Does your [dependent's medical thing] and your [specialty doctor] fall under the in-network providers for all the plans being considered?

2. Are the primary care physicians and specialists you and your family members visit in-network for all the plans being considered?

3. Are the imaging and lab services providers you use in-network for all the plans being considered?

These questions will help me understand if the in-network benefits of the plans are applicable to your family's healthcare usage.

After these questions were answered, it gave me the pros and cons of each plan based on my individual circumstance.

Now, again, as I've said a few times in this talk: I didn't just take its response for gospel. I read through the plans myself and I ran some numbers independently in order to verify the model's conclusion.

Ultimately, I ended up going with the health plan that ChatGPT came up with.

---

Working with large language models can feel like magic, and let's be honest: a lot of this stuff feels like magic.

But when you break it down, talking to a large language model is a lot like talking to an overconfident toddler (as best described by a good friend).

By using these tips and having a rough understanding what these large language models are doing under the hood, you will be able to take many of your mundane tasks and offload them to an extremely smart (yet possibly wrong) friend.

And with that, you are now a prompt engineering expert!

(You might be wondering: "what's the deal with that hero image?" I felt like this blog post was large enough that it needed a hero image, and because my mind is now exhausted, I asked my six-year-old daughter what I should use. She suggested a fluffy baby orange kitten with a fluffy baby puppy on its back in a grassy field with a sunrise in the background. I said "... good enough.")