Content strategy is changing now that LLMs are reading, writing, and rewriting most of what we publish. This series is a practical walkthrough for content folks: setting up the right tools, structuring content as markdown, defining tone of voice and microcopy in ways an LLM can actually follow, and evaluating what comes out the other end.
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What changes about content strategy when LLMs are both the writers and the readers. The shape of the series and who it's for.
The minimum viable toolkit for working with an LLM on content: where to write, where to store, how to keep humans and the model looking at the same source of truth.
Why markdown is the right substrate for LLM-era content, and how to structure your first files so they're useful to both humans and models.
How to capture tone of voice in a way an LLM can actually apply consistently — beyond vague adjectives, into concrete patterns and examples.
Generating and refining the small, high-stakes bits of text — buttons, errors, empty states — without losing voice or precision.
How to tell if the content an LLM produces is actually good. Lightweight evals for tone, accuracy, and fit, without drowning in process.
Pulling the threads together: a small, repeatable workflow for doing content strategy with LLMs in the loop.
“This is opening up all sorts of new neural pathways for me to see under the hood more of how the sausage is made! 🙏”
“Very timely at my enterprise software company as evaluation of AI features scales.”
“Everything I know about evals is from Peter's talk, which is why I'm back to find out more!”