IV · Retrieval & RAGEmerging★

CRAG

also known as Corrective RAG

Add a lightweight retrieval evaluator that grades each retrieved document and triggers corrective web search on poor retrievals.

This pattern helps complete certain larger patterns —

  • specialisesAgentic RAG★★— Replace static retrieve-then-generate with autonomous agents that plan, choose sources, retrieve iteratively, reflect, and re-query.

Context

A team is running a retrieval-augmented system in production over a corpus where retrieval quality varies request by request. Sometimes the top chunks are exactly right; sometimes they are tangentially related; sometimes they miss the answer entirely. The team cannot guarantee that every query gets a clean retrieval, and the cost of a hallucinated or confidently wrong answer is high enough that they need an explicit recovery path.

Problem

A naive retrieve-then-generate pipeline passes every retrieval — good or bad — straight into the generator without judging it. When the retrieval is poor, the generator either ignores it and falls back to parametric knowledge that may itself be wrong, or it incorporates it and produces an answer corrupted by irrelevant chunks. Either way, the user sees no signal that the retrieval was weak, and the system has no correction step that could fall back to a web search, refine the query, or refuse to answer when the evidence is insufficient.

Forces

  • Evaluator quality bounds correction accuracy.
  • Web fallback adds latency and external dependency.
  • Three-way grading (correct / ambiguous / incorrect) needs calibration.

Example

A RAG-powered legal assistant retrieves three statutes for a question about export controls; one of them is from the wrong jurisdiction. Naive RAG would hand all three to the generator and the wrong statute would corrupt the answer. The team layers in CRAG: a lightweight evaluator grades each retrieved document for relevance, the wrong-jurisdiction one falls below threshold, and the system triggers a corrective web search before generation. The final answer is grounded in two strong retrievals plus one fresh source instead of one bad one.

Diagram

Solution

Therefore:

After retrieval, a lightweight evaluator (T5-based or similar) grades each document as Correct, Ambiguous, or Incorrect. Correct documents go forward as-is. Ambiguous documents trigger a web search for additional evidence. Incorrect documents are discarded and replaced via web search. The generator receives the corrected document set.

What this pattern forbids. The generator sees only retrieval-graded-Correct documents, optionally augmented with corrective-search results.

The smaller patterns that complete this one —

Neighbourhood

Click any neighbour to follow the language. Scroll to zoom, drag to pan.

Used in recipes

Used in frameworks

References

Provenance