llms.txt vs robots.txt: What Actually Helps AI Discovery?

robots.txt gives crawl directives by user-agent and remains the practical control surface for mainstream crawlers. It is the right place to allow or disallow specific bots, restrict sensitive paths, and publish sitemap locations. It is widely implemented and operationally reliable.

llms.txt is typically used as a curated index of important pages and context for language-model tools. It may improve readability for agents that choose to consume it, but adoption and behavior are not standardized across platforms. It should not replace core technical SEO controls.

Keep robots.txt accurate and explicit. Maintain clean sitemap and canonical signals. If you publish llms.txt, keep it concise: top docs, key policies, and stable URLs. Treat it as supplemental guidance, not a substitute for crawl/index foundations.

Publishing llms.txt does not force indexing or citation. Blocking a bot in robots.txt can still override your visibility goals regardless of llms.txt content. Most visibility gains still come from content quality, clear structure, and trust signals.

Use a short list of canonical URLs to your primary docs, FAQ, and policy pages, plus a one-line site description. Update it when core URLs change. Keep the file stable so agents can rely on it without chasing outdated links.