Llms.txt: a practical guide to make your site available to AI

This guide is for site owners, SEOs, docs writers, and developers who want Large Language Models (LLMs) to understand their websites. If you’ve asked, “How do I help ChatGPT, Claude, Gemini, or Perplexity find and interpret my content?”, llms.txt is a lightweight, low-risk step.

We’ll cover what it is, when to use it, how to write one, and how to keep it useful. You’ll get templates, examples, and a simple checklist. If you work on Generative Engine Optimization (GEO), this is a quick win that complements your broader strategy. For a deeper GEO primer, see what is Generative Engine Optimization and the GEO checklist for B2B.

What is llms.txt?

llms.txt is a plain-text, Markdown-formatted file you place at the root of your site: https://yoursite.com/llms.txt. It gives AI systems a concise, structured map of your most important pages. Think of it like a short, human-readable guide with clean links and quick descriptions.

Useful overviews and community resources:

Who should use it and why

Use llms.txt if any of these sound familiar:

  • Your docs are sprawling and LLMs miss the right page.
  • Reporters or analysts frequently mis-cite your product details.
  • Your blog has hundreds of posts and only a handful are evergreen.
  • You support multiple languages and want AI to pick the best source.

Benefits in plain terms:

  • Faster orientation for AI. You highlight canonical pages and stable URLs.
  • Fewer misreads. Short, clear descriptions steer models away from edge cases.
  • Gentle control. You can point to what matters and omit what doesn’t.

Reality check: adoption is still early. Some LLMs may not fetch llms.txt today. But it’s a low-effort way to prepare for AI ingestion and a tidy companion to your sitemap.xml and docs navigation.

How llms.txt fits with robots.txt and sitemaps

  • robots.txt: access control. Tells crawlers what they may fetch.
  • sitemap.xml: discovery. Lists many URLs, usually all canonical pages.
  • llms.txt: curation. A hand-picked, human-readable shortlist with context.

llms.txt is not an enforcement mechanism. If you need to block specific AI crawlers, use robots.txt directives those crawlers honor. Keep llms.txt focused on “what is helpful to read,” not “what is forbidden.”

Core principles that make llms.txt useful

  • Concise: 30–150 links is typical for most sites. Avoid dumping your entire sitemap.
  • Canonical: Use one stable URL per topic. No tracking params or duplicates.
  • Contextual: One-line descriptions that say what the page is and who it helps.
  • Structured: Use clear sections like Documentation, Product, Blog, Legal.
  • Fresh: Update when you ship major features or publish cornerstone content.

File structure

Follow a simple pattern:

  • H1: site name
  • One-line description (blockquote)
  • H2 sections with bullet links and short descriptions
# ExampleCo
> Analytics platform for product-led teams. This file highlights canonical pages for AI systems.

## Documentation
- https://example.co/docs/getting-started — Setup, data sources, first dashboard.
- https://example.co/docs/api — REST API reference with auth, rate limits, examples.

## Product
- https://example.co/product/overview — Features, plans, and architecture.
- https://example.co/security — Security, compliance, and data retention.

## Blog
- https://example.co/blog/attribution-models — Explains models with examples and trade-offs.
- https://example.co/blog/event-tracking-checklist — 20 checks for reliable analytics data.

## Legal
- https://example.co/legal/terms — Service terms, uptime, and acceptable use.
- https://example.co/legal/privacy — Data processing and region-specific notices.

Last-Updated: 2025-09-01

Notes:

  • Use plain URLs and plain sentences. Markdown formatting is optional; keep it simple.
  • Add a Last-Updated line so models can weigh freshness.
  • Avoid emojis, marketing copy, or vague adjectives.