Corrective RAG (CRAG)

CRAG was introduced in the 2024 paper "Corrective Retrieval Augmented Generation" by Shi-Qi Yan and colleagues. It targets a basic weakness of standard RAG: the generator trusts the retriever. If the top documents are off-topic, outdated or only loosely related, the model often answers anyway. The paper's design adds a lightweight retrieval evaluator, a fine-tuned T5-large model in the original work, which scores each retrieved document for relevance to the question. Based on those scores, the system picks one of three actions. If at least one document is judged clearly relevant, the action is Correct: the documents go through knowledge refinement, a decompose-then-recompose step that splits them into small strips, scores each strip, and keeps only the useful ones. If all documents score low, the action is Incorrect: the retrieved documents are discarded, the question is rewritten into search-engine-style keywords, and web search results are used instead, again refined. If the evaluator is unsure, the action is Ambiguous, and the system combines refined internal knowledge with web results. The authors presented CRAG as plug-and-play: it can wrap standard RAG or other methods such as Self-RAG, and they reported improvements on several short-form and long-form generation benchmarks. In practice, many teams implement CRAG-style pipelines without the original fine-tuned evaluator, using an LLM prompt to grade relevance and a graph or agent framework (LangGraph has a well-known CRAG tutorial) to route between steps. Common misconceptions: CRAG does not verify that the final answer is correct; it grades retrieval inputs. Web search is not automatically better, since it can bring in low-quality sources. And grading adds latency and cost to every query.