Real llms.txt examples from leading tech companies (and what they got right)
Blog post from Mintlify
As AI agents increasingly serve as the primary interface for developers, the importance of documentation that caters to AI rather than humans has grown significantly. This shift necessitates a focus on providing clear, direct paths to essential information such as authentication steps and API references, as opposed to broad overviews. The concept of llms.txt, proposed by Jeremy Howard in 2024, addresses this need by offering large language models a curated guide to the most critical documentation pages, thus enhancing their ability to provide accurate answers. Various companies, including Vercel, Stripe, and Cloudflare, have implemented llms.txt to optimize their documentation for AI discovery, employing strategies that range from product-first organization to workflow-centric structures. This approach allows AI tools to prioritize high-value entry points and aids in delivering precise, contextually relevant information to developers, demonstrating a significant evolution in how documentation is structured and consumed in the era of AI-driven interactions.
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