llms.txt for documentation sites
Published:
Documentation sites are the ideal llms.txt use case: people constantly ask AI tools "how do I do X in your product," and a curated file points those models straight at the right pages.
Why docs benefit most
Your docs already are the answers. llms.txt lets you hand a model a clean index of them — getting started, guides, API reference — instead of letting it crawl navigation, search UIs, and marketing pages.
Generate it from your structure
Most docs sites already have a sitemap.xml. Group those URLs into a few sensible sections rather than dumping them flat:
# Acme Docs > Documentation for the Acme API: setup, guides, and the full endpoint reference. ## Getting started - [Quickstart](https://acme.com/docs/quickstart): First request in 5 minutes - [Authentication](https://acme.com/docs/auth): API keys and scopes ## Reference - [Endpoints](https://acme.com/docs/api): Every endpoint and parameter ## Optional - [Changelog](https://acme.com/docs/changelog): Release history
Link the Markdown versions
Many docs frameworks (Docusaurus, MkDocs, Mintlify, GitBook and others) can serve a Markdown or plain-text version of each page. Link those — page.md — so models parse clean content instead of rendered HTML with nav and sidebars.
Add llms-full.txt for full context
For larger doc sets, publish a companion llms-full.txt with the full text inlined, so an agent can ingest everything in one fetch. Keep llms.txt as the concise index.
Curate, then validate
Whether you hand-write the file or generate it from a plugin, review the output — auto-generators tend to include too much. See llms.txt examples for patterns, then validate to confirm the structure and that every link resolves.