A shift worth planning around: a growing share of the “developers” reading your documentation are not people. Coding assistants parse your docs inside the editor, and answer engines summarize them when a developer asks “how do I do X with your product.” Increasingly the first impression of your product is a machine’s paraphrase of your docs — and whether you’re the tool it recommends depends on whether your content is retrievable and citable.
Practitioners are responding on two fronts. First, structure: pages where one section cleanly answers one question, the golden path up top, descriptive link text over generic titles — the same discipline that makes docs good for humans makes them chunk-retrievable for models. Second, llms.txt: a curated Markdown index (think sitemap.xml for AI) that points models at your highest-value pages. Teardowns of leading files show clear patterns — Stripe mirrors its API structure, Anthropic ships a slim index plus a full export, Cursor and Windsurf organize around what a developer is doing right now.
The honest caveat: as of early 2026, no major AI provider has confirmed they actually read llms.txt during crawling, so treat it as cheap insurance, not a silver bullet. The reliable investment is the underlying content — accurate, well-structured, and answering the questions developers (and their assistants) actually ask. That compounds regardless of which standard wins.
The take
Treat "is my doc retrievable by an LLM" as a first-class docs metric alongside time-to-first-success. Structure pages so one section answers one question, lead with the golden path, and publish an llms.txt as a curated index — but don't over-invest in llms.txt specifically, since no major AI provider has confirmed they read it. The durable win is clean, chunk-retrievable docs, which help humans too.