Request to Implement llms.txt on Convex.dev
What is llms.txt?
Today, websites serve not only as a source of information for humans but also as a critical resource for large language models (LLMs). Language models enhance various tools, including development environments for coders, by utilizing information about programming libraries and APIs directly from website documentation.
Why Implement llms.txt?
Enhanced AI Integration:
AI tools often struggle to process entire websites due to limitations in context windows and the complexity of navigating HTML, JavaScript, and other web elements.
By implementing llms.txt, Convex.dev can ensure that the most relevant information is easily accessible to AI helpers, improving user experience and API usability.
Increased Adoption:
Platforms like Vercel AI SDK, Anthropic, Supabase, DuckDB, and ElevenLabs have already implemented llms.txt.
About llms.txt
An llms.txt file allows developers and organizations to curate and centralize the most crucial information in a format optimized for LLM consumption. While training models typically involve scraping all accessible content, llms.txt focuses on providing real-time, actionable data for users interacting with AI tools. For instance, instead of parsing through complex documentation, an AI-powered assistant can refer directly to llms.txt for concise and relevant answers.
Context Window Efficiency: AI tools—limited by their context window—can process information from llms.txt much more effectively than a full website scrape.
Ease of Access: The file reduces friction for users relying on AI-enhanced development tools.
Further Resources
For more details, you can visit the following:
llms.txt Official Website: https://llmstxt.org/
Directory of Services Using llms.txt: https://directory.llmstxt.cloud/
Convex can base the llms.txt file on its documentation and Convex Stack posts, ensuring it encapsulates all essential information for developers and AI tools.
Today, websites serve not only as a source of information for humans but also as a critical resource for large language models (LLMs). Language models enhance various tools, including development environments for coders, by utilizing information about programming libraries and APIs directly from website documentation.
Why Implement llms.txt?
Enhanced AI Integration:
AI tools often struggle to process entire websites due to limitations in context windows and the complexity of navigating HTML, JavaScript, and other web elements.
By implementing llms.txt, Convex.dev can ensure that the most relevant information is easily accessible to AI helpers, improving user experience and API usability.
Increased Adoption:
Platforms like Vercel AI SDK, Anthropic, Supabase, DuckDB, and ElevenLabs have already implemented llms.txt.
About llms.txt
An llms.txt file allows developers and organizations to curate and centralize the most crucial information in a format optimized for LLM consumption. While training models typically involve scraping all accessible content, llms.txt focuses on providing real-time, actionable data for users interacting with AI tools. For instance, instead of parsing through complex documentation, an AI-powered assistant can refer directly to llms.txt for concise and relevant answers.
Context Window Efficiency: AI tools—limited by their context window—can process information from llms.txt much more effectively than a full website scrape.
Ease of Access: The file reduces friction for users relying on AI-enhanced development tools.
Further Resources
For more details, you can visit the following:
llms.txt Official Website: https://llmstxt.org/
Directory of Services Using llms.txt: https://directory.llmstxt.cloud/
Convex can base the llms.txt file on its documentation and Convex Stack posts, ensuring it encapsulates all essential information for developers and AI tools.
llms-txt
A proposal to standardise on using an /llms.txt file to provide information to help LLMs use a website at inference time.
Discover websites embracing the llms.txt standard, designed to provide LLM-friendly content and guidance.
