Verified65% confidenceSolutionExact time
CRAG adds an evaluation-and-correction mechanism on top of standard RAG: it first scores the relevance of retrieved documents, and if the score falls below a threshold, it triggers corrective steps such as web search or query rewriting.
3
Sources
65%
Confidence
Long-term
Relevance
10/2/2026
First Seen
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Related Claims
UnverifiedRetrieval-Augmented Generation (RAG) reduces the probability of hallucinated content and enables traceable citations by anchoring model outputs to a verified document database.77% similarUnverifiedCorrective RAG (CRAG) scores the quality of results after retrieval and triggers a web search fallback when quality is low.75% similarUnverifiedQuery rewriting in CRAG has the LLM decompose the original question into several more precise sub-queries, retrieve results separately, and then merge them, so as to reduce retrieval failures caused by the vocabulary gap.75% similarUnverifiedTaking RAG as an example, interviewers will follow up by asking why Fine-tuning was not used, how to optimize when retrieval recall rate is low, and which vector database was used and why.75% similarUnverifiedIn retrieval-augmented generation (RAG) systems, the connection between an answer and its context is typically measured through vector similarity or cited passages.74% similar
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