KNWN Visibility
What is Retrieval-Augmented Generation (RAG)?
A KNWN glossary definition of Retrieval-Augmented Generation (RAG) in the context of AI visibility, search and brand discovery.
Definition
Retrieval-augmented generation (RAG) is an application architecture in which a system retrieves external material and supplies selected context to a generative model for a request.
Why it matters
RAG can use fresher or private information without changing base-model weights, but it introduces separate retrieval, authorization, grounding and citation failure modes. Evaluate retrieval before generation. OpenAI's retrieval guide documents vector-store search as an application capability.
Example
A support assistant retrieves permitted policy passages, includes their document IDs in context and cites the final answer. Tests verify access control, top-k retrieval, unsupported claims and stale documents.
Common misconceptions
- RAG retrains the model.
- Retrieved context is automatically correct.
- More documents always improve answers.
- Citations prove every generated claim is supported.
Interpretation note
Definitions describe terminology; they do not establish an official ranking factor or guarantee an outcome. Confirm platform-specific terminology against primary documentation.