Standard retrieval-augmented generation retrieves documents and hands them straight to the model to generate a response, trusting that the retrieval step found something genuinely relevant. Corrective RAG adds a checking step in between: after retrieving candidate documents, the system evaluates whether they actually address the query with enough confidence, before letting the model use them.

If the retrieved documents fail that check, low relevance, an apparent mismatch with the query, CRAG can trigger a broader search, fall back to a different source, or flag the case for human review, rather than generating a confident-sounding answer grounded in the wrong document, exactly the failure mode plain RAG is most vulnerable to.

Frequently Asked Questions

What specific problem does CRAG solve that standard RAG doesn't?

Standard RAG has no built-in check on whether the documents it retrieved are actually good matches for the query, it just uses whatever came back. CRAG adds that relevance evaluation step, catching cases where retrieval quietly failed before the model ever generates a response from bad context.

How does the correction step actually work?

A separate evaluation, sometimes another model call, scores the retrieved documents' relevance to the query. Documents below a confidence threshold get discarded, and the system either broadens its search, tries an alternative retrieval method, or escalates rather than proceeding with weak context.

Does CRAG add noticeable latency compared to standard RAG?

Yes, the extra evaluation step takes additional time and often an additional model call, a real cost worth it for use cases where an ungrounded, confidently wrong answer is a bigger risk than a slightly slower response.

Is CRAG only useful for enterprise or high-stakes use cases?

It adds the most value wherever a wrong, confidently-stated answer carries real cost, a compliance question, a financial figure, a policy interpretation, versus a low-stakes, casual query where standard RAG's occasional miss is a tolerable tradeoff for lower latency.

Can CRAG eliminate hallucination entirely?

No, it specifically reduces the risk of the model confidently using irrelevant retrieved context, but a model can still generate an inaccurate response for other reasons even with genuinely relevant source material in front of it.

How does an AI agent decide what counts as a good enough relevance score?

That threshold is typically configured by whoever builds the system, tuned against real examples of good and bad retrievals for that specific use case, rather than a single universal number that applies the same way to every deployment.