Build a spec-compliant llms.txt file for your website in under a minute. Fill in your site details, add your key pages, then copy or download the file and drop it at your web root.
Follows the llmstxt.org proposal. Everything runs in your browser: nothing is uploaded or stored.
Group your most important URLs into sections. Use "Optional" for secondary pages LLMs can skip when context is tight.
# Acme Analytics > Acme Analytics is a product analytics platform that helps SaaS teams understand user behavior and reduce churn. Acme serves 2,000+ B2B customers. The docs below cover setup, APIs, and integrations. ## Docs - [Quickstart](https://acme.com/docs/quickstart): Install the tracker and send your first event in 5 minutes - [API Reference](https://acme.com/docs/api): REST endpoints, authentication, rate limits ## Blog - [Churn benchmarks 2026](https://acme.com/blog/churn-benchmarks): Original data from 1,400 SaaS companies ## Optional - [Pricing](https://acme.com/pricing) - [About](https://acme.com/about)
Upload this file to your web root so it resolves at yoursite.com/llms.txt. See ours live at geonimo.com/llms.txt.
llms.txt is a proposed web standard: a markdown file at the root of your site that gives large language models a concise, curated map of your most important content. Context windows are limited and full HTML pages are noisy, so llms.txt hands AI systems exactly what matters, in a format they parse natively.
The format is deliberately simple. An H1 with your site name, a blockquote with a one-line summary, optional context paragraphs, then H2 sections listing your key pages as markdown links with short descriptions. A special ## Optional section marks pages an LLM can skip when it needs to save tokens.
For a deeper dive into the standard and how it fits your AI search strategy, read our llms.txt glossary entry or the full GEO Guide.
From generated file to live standard in a couple of minutes.
Place the file in your public or static folder so it deploys to yoursite.com/llms.txt. On Vercel, Netlify or any static host, that is a one-file commit.
Open yoursite.com/llms.txt in a browser. You should see raw markdown, served as plain text. If your router rewrites unknown paths, add an exception for the file.
Revisit it when you ship major pages. A stale llms.txt pointing at dead URLs is worse than none: it teaches models the wrong map of your site.
llms.txt is a plain-markdown file placed at the root of your website (yoursite.com/llms.txt) that gives large language models a curated, token-efficient map of your most important content. Proposed by Jeremy Howard of Answer.AI in September 2024, it uses a simple format: an H1 with your site name, a one-line summary quote, optional context, and H2 sections listing your key pages as markdown links.
Adoption is voluntary and still emerging. Documentation platforms like Mintlify generate it automatically, developer tools and AI agents increasingly fetch it, and thousands of sites have published one. The major AI labs have not formally committed to it yet, but the cost of adding one is near zero and it positions your best content for any engine that does consume it.
robots.txt controls which crawlers may access which paths. sitemap.xml is an exhaustive machine-readable list of every URL for search indexing. llms.txt is different: it is a curated, human-written guide that tells an LLM which pages matter most and what your site is about, in markdown the model can ingest directly.
llms-full.txt is a companion convention: instead of linking to your key pages, it inlines their full content into one large markdown file so an LLM can read everything in a single request. It is most common for documentation sites. Start with llms.txt first; add llms-full.txt if your docs are the product.
Upload it to your web root so it resolves at https://yoursite.com/llms.txt, served as plain text (UTF-8). On most stacks that means dropping the file in your public or static folder and deploying. Then open the URL in a browser to verify it renders as raw markdown.
It is one signal among many, not a silver bullet. llms.txt makes your best content easier for AI systems to find and ingest, but citations in ChatGPT, Perplexity or Google AI are earned through citable content, third-party mentions, structured data and authority. Treat llms.txt as cheap, sensible hygiene inside a broader GEO strategy.