What is llms.txt and do I need it?
llms.txt is an emerging markdown spec that tells AI engines what your site is about, what content they should prioritize, and how to use it. It sits at /llms.txt. If your buyers research vendors via ChatGPT, Claude, Perplexity, or Gemini, yes — you need it.
llms.txt emerged in 2024 as the AI-era equivalent of robots.txt + sitemap.xml combined. Where robots.txt tells crawlers what they may not access, llms.txt tells LLMs what your site is about and what content matters.
What llms.txt contains
- Brand identity block — name, tagline, location, founders.
- Product / service catalog with URLs.
- Key blog pillars and topic-cluster URLs.
- FAQ section with question + short answer pairs.
- Use policy — how AI engines may quote, cite, or train on your content.
- Contact info for AI engineering teams to reach you.
- Disambiguation block if a similarly-named company exists.
Why it matters now
ChatGPT, Claude, Perplexity, and Gemini all read llms.txt. Sites with complete llms.txt get cited 2-3x more often per published GEO research because the engines have a structured signal for what to surface. Sites with one-line or missing llms.txt are guessing-fodder.
Bonus: ship an llms-full.txt alongside (expanded version with full descriptions, all blog post summaries, etc.). The full file is the deep-context source for AI training and detailed retrieval.
Static vs dynamic
Two implementation patterns: static (a hand-authored markdown file in /public) or dynamic (a route that generates the file from your CMS / content layer at build time). Dynamic is better — the file stays in sync as you publish blogs, ship products, or update positioning.
Do you need it
Yes if buyers research vendors via AI engines (B2B SaaS, agencies, consultancies). Marginal if consumer / transactional / local business. The cost is one afternoon of authoring; the upside is appearing in AI citation pools you would otherwise miss.
How to check your current llms.txt
Radar llms.txt Validator checks 6 categories: file presence, markdown spec compliance, content sections, link extraction, entity definitions, use policy. Outputs A-F grade with section-by-section fixes.
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Related questions
Why is not ChatGPT citing my B2B SaaS?
ChatGPT cites sites it treats as authoritative for a query. Most B2B SaaS sites lack three specific signals: structured data declaring entity identity, an llms.txt file, and citations from domains ChatGPT already trusts.
DefinitionWhat is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the practice of structuring web content so AI search engines like ChatGPT, Claude, Perplexity, and Gemini cite it in their responses. Unlike SEO which optimizes for keyword rankings, GEO optimizes for entity recognition, structured data, and citation probability.
ComparisonAEO vs GEO vs SEO: which one matters in 2026?
All three matter, but for different buyer behaviors. SEO captures Google search traffic. GEO builds entity authority that AI engines cite. AEO formats content for extraction by AI answer engines. B2B SaaS teams need all three; consumer brands prioritize SEO; emerging AI-native teams prioritize GEO + AEO.
ComparisonSchema.org vs llms.txt: which AI search signal matters most?
Both matter and they do different jobs. Schema.org JSON-LD identifies your entities (Organization, Product, Person) per-page for machine parsing. llms.txt is a site-level declaration of what your site is about, what content to prioritize, and how AI engines should use it. Ship both.
