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Roaming RAG – Make the Model Find the Answers

Roaming RAG offers a fresh take on Retrieval-Augmented Generation, letting LLMs navigate well-structured documents like a human—exploring outlines and diving into sections to find answers. Forget complex retrieval setups and vector databases; this streamlined approach delivers rich context and reliable answers with less hassle. It’s perfect for structured content like technical manuals, product guides, or the innovative llms.txt format designed to make websites LLM-friendly.

Since Roaming RAG directly navigates the text of the document, there is no need to set up retrieval infrastructure, and fewer moving parts means less things you can screw up! Similar to /sitemap.xml and /robots.txt, llms.txt is intended to be read by machines, and it serves as a structured guide to help large language models quickly understand the key information about a website or project. This will give LLM-based assistants the ability to do all sorts of neat things – like answer questions about someone's public CV page, describe courses offered at educational institution, or provide programmatic examples for a software library.

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