This Is How You Get Good at AI
Use it for everything and keep an AI Journal.
Once a week someone asks me if I have any tips on how to get good at using AI. I try my best to deliver the truth as best I can — without sounding dismissive. You kind of just need to think a lot, I want to say. Like, more than you normally do.
Now, forgive me a moment for the pith. But if you’re in the same space as I am, you know what I mean. Using AI “well” — so far — isn’t and hasn’t been about scientifically engineering language in a complex and perfect chain that unlocks a some mysterious 10x magic.
It’s just about thinking well.
Let me share a story that illustrates this.
I was listening to The Economist’s podcast Boss Class a few weeks ago. They sent their senior writer, Andrew Palmer, to answer the question “How should employees and bosses be using AI right now?” Ethan Mollick — your friendly neighborhood Wharton professor who wrote Co-Intelligence — told him to use it for everything. Literally. The thesis? Find the “jagged frontier” yourself.
I'm with Mollick on this. But as I listened, something more specific caught my attention. The first thing Palmer did was start an audio diary. Nobody told him to. Nobody assigned it (as far as I am aware.) He just started recording what was happening as he used AI more intensively.
His diaries are a part of the podcast. You can hear him processing it in real time. What was working. What felt wrong. What surprised him.
It seems like a natural instinct. The experience demanded reflection.
Ludwig Siegele, The Economist’s tech editor — a guy who’s been covering technology for over 25 years — described a feeling that I think is becoming so ubiquitous it may need a name soon. Working with generative AI, he said, you’re always “teetering at the edge of disappointment.” Some days it’s extraordinary. Other days it’s junk.
That frustration is often what turns people away. But if you combine Mollick’s advice with Palmer’s action — creating an audio diary — you actually capture the sweet spot where learning happens. That constant oscillation between amazement and frustration is actually data that creates new guardrails. It's what teaches you where the boundaries are. But if you're not paying attention to it, you miss it entirely.
By the end of the experiment, Palmer boiled his experience down to three lessons: experiment widely, reflect on what you find, and know yourself. Easier said than done, eh?
But what stands out to me most is lesson two — reflect on what you find. That’s the one that lives in within our locus of control. Know thyself is as old as time, experiment widely only works if I have time, but reflect on what you find? I can control that. I can do that. Why didn’t anyone tell me?
From my perspective, this is the space that nobody’s building tools for. There are a thousand products that help you use AI. Prompt libraries, workflow automations, AI-powered everything. But almost nobody is building tools that help you think about your AI use. That help you notice what’s actually happening when you hand part of your brain over to a machine.
I know this because I stumbled into it myself.
Last spring, I was in my MFA program, revising my novel manuscript. Part of my research involved working with AI on creative writing — testing what it could and couldn’t do, paying close attention to where it helped and where it got in the way. I wasn’t just using AI to determine if it was a better writer than me. I was studying what happened when I used it, both to the quality of the work and to me as a person.
And I started keeping a journal. Before every AI session, I’d write down what I was trying to do and how I planned to approach it. I'd also stop halfway through and revert back to my Word doc, typing out whatever the AI session had shaken loose. And then at the end of every session, I’d write down what I noticed. What worked. What didn’t. What surprised me. Even how I felt about it. That was the good stuff.
One session produced a breakthrough — an insight about my own creative process that I absolutely would have missed if I hadn’t been writing things down. The AI didn’t give me the insight. The journal did. The practice of pausing before and after, of putting words to the experience, is what made the invisible visible.
That’s when I thought: what if everyone did this?
Not everyone revising a novel. Not just writers or students or researchers. Anyone who uses AI regularly. What if the missing piece isn’t better prompts or fancier tools — but the simple habit of reflecting on how you use them?
There’s a growing movement called Slow AI — researchers, designers, artists pushing back against the race to automate everything. I’m not trying to start a movement. I just built a journal. But the instinct is the same: slow down, pay attention, decide for yourself what AI should and shouldn’t do for you.
That instinct is starting to get research behind it. Last week, Harvard Business Review published
Last week, Harvard Business Review published an in-progress study showing that AI doesn’t reduce work — it intensifies it. Workers speed up, take on more, blur the line between work and rest, and burn out. The researchers’ recommendation? What they call an ‘AI practice’ — intentional pauses built into your workflow to assess what you’re doing and why. That’s exactly what this is.
So I built The AI Journal. It’s a simple web app. Free. Can take about two minutes per session, or longer if you are so inclined.
Before an AI session, you answer three quick questions: What are you working on with AI today? What’s your plan? How are you feeling going in?
Then you go do your work.
The timer will track your time in session. When you come back, three more questions: How’d it go? Anything surprise you? What would you do differently next time? And there’s a field for saving any tips, tricks, or strategies that actually worked — things worth remembering.
You can type or just talk into your phone or your computer. It transcribes your voice automatically. Your reflections are private and only visible to you.
Over time, your entries become two things at once. First, they become a mirror — a record of how you work with AI, what patterns you fall into, what you tend to reach for, and what you tend to avoid. Second, they become a playbook — a personal collection of strategies, workflows, and lessons that you’ve actually tested and validated yourself. Not someone else’s prompt library. Your own tested strategies, saved in one place, monitored in real-time.
If you’re a writer worried about losing your voice, the journal helps you see exactly where AI is helping and where it’s quietly replacing you. If you’re a developer optimizing your workflow, it captures the tricks worth keeping. If you’re a student, it forces you to ask whether AI is actually teaching you something or just giving you answers you haven’t earned. If you’re a manager trying to figure out how AI fits into your team’s work, it gives you a structured way to think about it instead of just guessing.
Mollick told Palmer to use AI for everything. Palmer’s instinct was to keep a diary of what happened. The AI Journal is that diary — but structured, so you don’t have to invent the practice from scratch.
Here’s what I’m asking. Try it for a week. Just one week. Before and after every AI session, spend two minutes reflecting. That’s it. Don’t change how you use AI. Don’t try to use it more or less. Just pay attention.
See what you notice about yourself.
After your week, I want to hear what you found. What did you notice? What surprised you? Did anything change about how you work with AI?
I’m building something, and I don’t think I’m the only one who needs it. I think anyone who uses AI regularly — and is honest about the fact that they’re still figuring it out — could use a place to write down what they’re learning. Not a course. Not a certification. Just a journal.
The people who are going to be the best at AI aren’t the ones with the best prompts. They’re the ones who are paying attention.
I don't do this in a formal way but I actually reflect a lot on how I use it within ChatGPT itself as I'm working and things pop up. I'm constantly thinking about my thinking. I think it's instrumental in teaching students, especially, but everyone need to increase this skill, because otherwise the tool gradually just takes over.
This landed hard for me because I accidentally ran exactly the experiment you're describing, without realizing that's what I was doing.
I was trying to teach Claude my writing style (my readers kept saying my Substack sounded AI-generated, which, fair). So I started answering Claude's questions about how I write. One question at a time, low stakes, just talking. By question three I'd accidentally written the most authentic thing I'd produced in years. About ADHD, about being paralyzed by blank pages, about the two different writers living in my head - the one who shows up in conversation and the one who tries to sound like a "Real Writer" in public.
The journal didn't give me the insight. But the act of REFLECTING on my process, being forced to put words to what I was doing and why, surfaced things I'd been carrying for years without seeing them. Claude didn't discover my voice. The reflection did.
What you said about "teetering at the edge of disappointment" resonated. That oscillation between amazement and frustration IS the signal. But I think there's a state you didn't mention: the moment where the AI shows you something about yourself that you weren't looking for. That's not amazement or frustration. It's something closer to being caught off guard by your own reflection in a window you thought was a wall. (That metaphor kind of got away from me, but I'm keeping it.)
Going to try The AI Journal this week.