Question 1

What is llms.txt and why does it matter?

Accepted Answer

llms.txt is a plain-text file placed at the root of your domain (e.g. https://example.com/llms.txt) that tells AI crawlers — ChatGPT, Perplexity, Claude, Gemini — which pages best represent your site when they need to summarise or cite it. It follows a lightweight Markdown spec proposed by Answer.AI: a title, an optional summary block, and a list of page links with descriptions. Without it, AI systems guess which pages to surface, often choosing low-value or outdated content. With it, you steer them to your money pages.

Question 2

How is llms.txt different from robots.txt and sitemaps?

Accepted Answer

robots.txt tells crawlers which pages to avoid. Sitemaps list every indexable URL for search engines. llms.txt is purpose-built for AI context windows: it is a curated, human-readable shortlist of the pages that best represent your brand and expertise. Where a sitemap might have tens of thousands of URLs, a well-crafted llms.txt has 10 to 50 — chosen to fit inside the context a language model can actually reason over.

Question 3

Which AI crawlers read llms.txt?

Accepted Answer

Perplexity confirmed support in 2024. ChatGPT's browsing agent, Claude.ai, and several AI SDKs retrieve llms.txt when available. The spec is still emerging — not every system honors it today — but early adoption places you ahead of competitors who are invisible to AI answers. Think of it as robots.txt in its first year: the crawlers that matter are already checking.

Question 4

How many pages should I list?

Accepted Answer

Keep it focused. The spec recommends listing your most important pages — not every URL. A good rule: start with your homepage, your primary service or product pages, your strongest case studies or proof pages, and any deep authoritative content you want AI systems to cite. 10 to 30 entries is a practical range for most sites. More than 50 entries risks diluting the signal.

Question 5

What goes in the page description field?

Accepted Answer

Write a single plain-English sentence that says what the page is about — the kind of sentence an AI would use to summarise the page to a user. Avoid keyword stuffing. The description is read by language models, not keyword matchers. Clear, specific, and honest outperforms optimised every time. Example: "Three verified case studies showing organic traffic growth from paid-to-organic shifts across SaaS, fintech, and travel clients."

Question 6

Where do I upload the file once generated?

Accepted Answer

Place llms.txt in the root of your public website directory so it resolves at https://yourdomain.com/llms.txt. On most web servers (Nginx, Apache, Netlify, Vercel, Cloudflare Pages) this means adding a file named llms.txt to your public or static output folder. The file must return a 200 status code with content-type text/plain. Verify with curl -I https://yourdomain.com/llms.txt after deployment.

Question 7

Should I also create an llms-full.txt?

Accepted Answer

The spec defines an optional llms-full.txt that includes the full text of your key pages inline rather than just links. It is useful for AI systems that cannot follow URLs (e.g. some SDK-based agents). Build llms.txt first, validate that AI crawlers can access it, then consider llms-full.txt for your top 5 to 10 pages if you see evidence that agents are not following through to your pages.

Question 8

Does having an llms.txt guarantee AI citations?

Accepted Answer

No. llms.txt improves discoverability and steers AI crawlers to your best content, but citation is ultimately determined by relevance, authority, and how well your content answers the query. It is a necessary signal in a multi-factor system, not a magic bullet. Pair it with strong entity coverage, citation-worthy statistics, and clear authorship — the same foundation that drives traditional SEO, applied to AI retrieval.

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Frequently asked questions

llms.txt is step one. AEO strategy is the rest.