What Is llms.txt? How to Use It for AI Search Visibility

llms.txt is a plain-text map of your site for AI crawlers and assistants — similar in spirit to robots.txt, but written for models. Here is what it is, what it is not, a practical template, and how to measure whether it changes citations.

llms.txt is an emerging convention: a markdown or plain-text file at your site root that gives AI systems a curated map of what you want them to understand about your product, docs and key pages. Think of it as a receptionist for machines — short, structured and honest — not a magic ranking switch.

What llms.txt is for

• Point AI crawlers and assistants at your canonical product, pricing, docs and answer-ready guides.
• State who you are, what you do, and which URLs carry the authoritative definitions.
• Reduce the chance a model summarizes an outdated mirror or a thin third-party page about you.

What llms.txt is not

• Not a guarantee of ChatGPT citations. Engines still retrieve, rank and generate on their own policies.
• Not a replacement for answer-ready content. If the pages you list are thin or promotional, the file only advertises weak material.
• Not the same as robots.txt. robots.txt governs crawl permission. llms.txt is editorial guidance. Keep both accurate.

A practical skeleton

Host it at https://yourdomain.com/llms.txt as text/plain. Keep it short enough to scan:

• One-line product definition at the top.
• Links to pricing, core product pages, and your best answer-first guides.
• Clear entity facts (what you are / are not; independence notes if relevant).
• No inflated claims, undated case studies or keyword stuffing.

jujuGEO publishes its own at jujugeo.com/llms.txt, generated from the same article registry that powers /learn so the map cannot drift from the live guides.

Pair it with crawl hygiene

llms.txt helps only if the linked pages are crawlable and clear: fast HTML, sensible headings, valid Article/FAQPage schema where appropriate, consistent brand naming, and an up-to-date sitemap. Blocked assets, soft-404s and contradictory product facts still hurt retrieval.

How to know if it helped

Do not declare victory because the file returns 200. Treat publish as a hypothesis:

1. Record baseline AI answers for a fixed buyer-question set.
2. Ship llms.txt plus any linked answer-ready pages.
3. Re-probe the same questions on a cadence across engines.
4. Look for changes in mention rate, cited-instead domains and share of voice — not vibes.

How jujuGEO fits

jujuGEO measures whether engines cite you for buyer questions and drafts gap-specific fixes. llms.txt is one discoverability tactic inside that loop, not a substitute for it. Check your current standing with the free AI visibility check, then keep prompts on a schedule if you invest in crawler and content changes.