Using LLMs and agents with Xcity docs

Drop-in context for AI assistants — llms.txt, semantic anchors, structured references.

We publish docs in a shape that’s easy for LLM-powered tools (Cursor, Continue, Claude, ChatGPT) to ingest.

llms.txt

/llms.txt and /llms-full.txt follow the llms.txt convention:

  • /llms.txt — a curated index pointing at every important reference page.
  • /llms-full.txt — the same index plus the full text of every doc concatenated, suitable for one-shot context loading.

Both files regenerate on every build. They’re committed to the deploy so AI tools that crawl them get a consistent, fresh view.

Markdown source

Every doc page can be fetched as raw markdown by appending ?raw to the URL:

GET https://xcity.one/docs/en/api-reference/inference?raw

This skips the wrapper layout and returns the markdown content + frontmatter. Useful when wiring Xcity docs into a RAG pipeline.

Stable anchors

Every <h2> and <h3> gets a slug derived from the heading text and rendered as id="...". These slugs are stable across edits — when we rename a heading, we add a backwards-compat anchor.

Cite-friendly structure

Each page has:

  • A single <h1> matching the frontmatter title
  • A 1-2 sentence summary right under it (= frontmatter description)
  • Headings in semantic order, no skipping levels
  • Tables for matrix-style facts (plans, error codes, SLAs)

That structure plays well with extractive summarizers and quote-citation tools.

Last updated: