Content Strategy for LLMs
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.
What will we discuss?
- What changes about content strategy when LLMs are both the writers and the readers
- The minimum viable toolkit for working with an LLM on content
- Why markdown is the right substrate, and how to structure your first content files
- How to capture tone of voice so an LLM can actually apply it
- Generating and refining microcopy without losing voice or precision
- Lightweight ways to evaluate the content an LLM produces
- A small, repeatable workflow for content strategy with LLMs in the loop
Who is this for?
- Content strategists, UX writers, and content designers adapting to LLMs
- Anyone responsible for tone of voice or microcopy in a product
- Teams that want a practical, low-process way to put LLMs into their content workflow
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1. Content Strategy for LLMs
Introduction
What changes about content strategy when LLMs are both the writers and the readers. The shape of the series and who it's for.
Setting up your tools
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.
First markdown content files
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.
Tone of voice
How to capture tone of voice in a way an LLM can actually apply consistently — beyond vague adjectives, into concrete patterns and examples.
Microcopy
Generating and refining the small, high-stakes bits of text — buttons, errors, empty states — without losing voice or precision.
Evaluating content generation
How to tell if the content an LLM produces is actually good. Lightweight evals for tone, accuracy, and fit, without drowning in process.
Wrapping up
Pulling the threads together: a small, repeatable workflow for doing content strategy with LLMs in the loop.
What participants say
"This training has provided valuable insights into AI product development methodologies and practical implementation strategies."
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