What is Corrective RAG or CRAG?

Corrective RAG, introduced by Yan and colleagues in 2024, evaluates retrieved documents with a lightweight relevance classifier and takes different actions based on confidence. High-confidence retrievals proceed to generation directly, ambiguous cases trigger web search for additional context, and low-confidence cases rewrite the query and retry. This reduces hallucinations caused by irrelevant retrieved context, a major failure mode in naive RAG. CRAG adds classifier overhead but substantially improves answer quality on queries where the corpus is incomplete.