Building Reliable RAG Systems
Retrieval-augmented generation (RAG) gives a language model access to external evidence at inference time. It is useful when answers depend on private, changing, or domain-specific information that should not be encoded only in model weights. RAG does not make an answer correct by construction. A system can retrieve the wrong document, omit a decisive table row, use an obsolete version, misunderstand accurate evidence, or attach a citation that does not support its claim. Retrieval changes the failure surface; it does not remove it. ...