Glossary
RAG for docs
Definition
RAG for docs — retrieval-augmented generation over documentation — is a pattern in which an AI assistant answers a question by first retrieving the relevant passages from a documentation set, then generating an answer grounded in those passages and, ideally, citing them.
Why retrieval instead of training
A model's training data is frozen at a point in time and doesn't include your product's current documentation. Retrieval reads the documentation as it is today, at the moment the question is asked. When the docs change, the next answer changes with them — no retraining.
Retrieval also makes answers checkable. An answer that cites the section it came from lets the reader confirm it in one click, which an answer from a model's memory cannot offer.
The answer is only as good as the source
RAG moves the accuracy problem; it does not solve it. If a page says a parameter is optional and it is now required, the assistant will say so too — confidently, and with a citation that makes the wrong answer look verified.
That makes documentation accuracy the ceiling on a docs assistant's accuracy. Teams that deploy a support chatbot over stale docs find the chatbot repeating the staleness at scale.
What makes documentation retrievable
- Self-contained sections that make sense when lifted out of the page around them
- Headings that name the thing — the endpoint, the setting, the task — rather than clever titles
- Consistent terminology, so a question phrased one way finds content phrased the same way
- Clean text, available without navigation and page chrome mixed in
- Coverage of the question — retrieval can only find what was written
Where you meet it
Support chatbots on documentation sites, assistants built into products, and coding agents that query a docs source through an MCP server while they work. The pattern is the same in each: find the passage, answer from it, show where it came from.
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Questions people ask
Does RAG stop an assistant from hallucinating?
It reduces it, because the model answers from retrieved text rather than memory. It doesn't eliminate it, and it can't fix documentation that is itself wrong. Citations let the reader check.
How is RAG different from llms.txt?
llms.txt is an index that tells an agent what pages exist. RAG is the process of finding the right passage for a question and answering from it. An index can feed a retrieval system; it isn't one.
Do we need to rewrite our docs for RAG?
Rarely. Clear headings, self-contained sections and consistent terms help human readers and retrieval alike. The bigger risk is inaccuracy, not formatting.