I Fed My Essays to ChatGPT Until It Learned My Voice

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Every time I publish a new essay, I go through the same ritual.

I download a Word document of the final draft and upload it to the project I have set up for my Every column Working Overtime in ChatGPT. Once it's there, I prompt it with one of my favorite AI incantations: What do you notice?

The model responds with a readout of how the new input changes its "theory of Katie"—the features it thinks characterize my writing. Here’s an example of a pattern it noticed: I start with a moment of friction (a failed demo, for example), zoom out to the big picture, complicate the issue, and end with a usable framework.

Guilty as charged.

I hadn't realized I was following this playbook until I saw it reflected back to me. Once I saw it, I couldn't unsee it: the steps my writing almost always follows, as deeply ingrained as the fifth-grade dance steps I still know by heart.

Here’s the next step in the ritual: I take the patterns and principles I agree with—signature moves I want to enhance, foibles I want to avoid—and add them to my personal “style guide”: a Google Doc full of guidelines and examples that train the model on how to think like me. Plugged into the project files of a ChatGPT or Claude project, it functions as a “writing brain” that brings out my best work.

I’ve written before that you’ll tear my Working Overtime project from my cold, dead hands. By the time you’re done reading this, you’ll see why. You’ll see how I built it, how I use it, and why you might want to think about creating an AI writing brain of your own.

Building the system

I first stumbled into ChatGPT projects—personalized workspaces inside ChatGPT where you can group together related chats, and add files and custom instructions to inform those chats—because I was tired of re-entering the same context, like formatting templates and style guides, every time I wanted to generate an outline for a new piece. Projects let you attach a Google Doc as background context on which the AI can draw for every chat started inside that project. I set up projects for a few of my freelance clients and noticed how much easier it became to produce work that met my clients’ standards.

One afternoon when I was bored and—if I’m being honest—probably procrastinating, I started to wonder what would happen if I showed ChatGPT my work. So I rounded up a few examples of work I was proud of and gave it to ChatGPT to analyze.

As soon as I saw the analysis that came back, I knew I wanted to save it. So I opened a doc, named it “Working Overtime Style Guide,” and started dropping in ChatGPT’s observations. It was just a collection of random observations at first, but over time, it evolved into a kind of living playbook—part checklist, part manifesto—that captures not just how I write, but how I want to keep writing.

From there it became recursive. Each essay I published went into the system. Each time I added a new example to the project and asked again, “What do you notice?” it surfaced new patterns worth adding to the style guide. Each addition sharpened the rules the model would apply in working with me on my next draft. The project turned into a writing space that knows me, remembers me, and pushes me to be more myself on the page.

This process of curating the information you feed a model to improve its performance has been called context engineering. It may sound intimidating to a “non-technical” person because it has the name “engineering” in it, but all it really is is deciding what examples, rules, and patterns to show the AI so it learns to work the way you need it to. As our consulting trainer Alex Duffy noted in describing his experience building AI Diplomacy, context engineering is less like engineering than it is like playing music. You have to know what keys to press or strings to hold to strike the chord, but it’s ultimately the messier human skills of intuition and timing that turn scattered notes into something coherent.

Software engineers do it by capturing code reviews, architectural decisions, and debugging lessons in files that teach AI their standards. Writers can do it by feeding the model their best essays, documenting their voice patterns, and building style guides that enforce their taste—same recursive loop, different medium.

What’s in the guide

Sharing this feels like opening up my diary, but I know this is what you’re here for—the guts of the system, the part everyone asks me about when I tell them about my “writing brain.” So here it is: what lives inside the Working Overtime style guide.

Overview and mission

This is the north star. It’s the reminder of what Working Overtime is here to do and what every draft needs to achieve.

Core directives