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Technical

llms.txt

A proposed standard file placed at a website's root that provides large language models with a curated, machine-readable summary of the site's most important content. It functions like robots.txt for the AI era, guiding how generative engines read and represent a brand.

Detailed Explanation

llms.txt is an emerging proposed standard: a Markdown file placed at the root of a website (for example, /llms.txt) that gives large language models a concise, curated map of the site's most important content. Where robots.txt tells crawlers what they may or may not access, llms.txt goes further by highlighting and summarizing the pages a model should prioritize when reasoning about the site (documentation, key product pages, policies, and authoritative explainers) often with links and short descriptions in clean, easily parsed text. Adoption is still early and not universally honored. Importantly, Google has stated plainly that it does not use llms.txt: in its guide to optimizing for generative AI search, Google says you don't need to create new machine-readable files, AI text files, or special markup to appear in Google Search or its generative AI features, because Search itself doesn't use them, and that maintaining an llms.txt for other services neither helps nor harms your Google rankings, since Google simply ignores it. In other words, treat llms.txt as a low-cost, optional signal aimed at the other AI systems and tools that do consume it, not as a Google ranking tactic. Solid crawlability, clean HTML, and structured data remain the dependable fundamentals.

Examples

1

An llms.txt that links a model to your core product docs, pricing, and a brand overview in plain Markdown

2

Maintaining an llms.txt for AI tools and services that consume it, while knowing per Google's own guidance that Google Search ignores it

3

Pointing AI engines that support the file to the canonical version of frequently duplicated content

Why It Matters

llms.txt lets you actively shape how AI models read your site instead of leaving it to chance, improving the accuracy and prominence of your brand in generated answers.

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