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Métriques d’évaluation RAG et RAGAS
Technique
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RAG Correctif (CRAG)
Corrective RAG (CRAG) is a retrieval-augmented generation method that checks how relevant the retrieved documents are before answering, and takes a corrective action when retrieval looks weak, such as refining the documents, rewriting the query or searching the web.
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Aperçu
It matters because standard RAG passes whatever it retrieves to the model, and irrelevant or misleading documents are a major cause of wrong, confidently stated answers.
Plongée profonde
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 If the top documents are off-topic, outdated or only loosely related, the model often answers anyway.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.
Impact stratégique
Coût et budget
Les décisions en matière d'architecture déterminent les performances et les coûts d'exploitation pendant des années.
Décisions plus claires
La formation technique aide les équipes à choisir la bonne pile, pas seulement la plus récente.
Contrôle qualité
De meilleurs choix d’ingénierie réduisent les incidents de fiabilité en production.
The Future of Corrective RAG (CRAG)
The CRAG idea, check retrieval quality and route accordingly, has become a common pattern in agentic RAG systems, where a model decides whether to retrieve again, search elsewhere or answer. Future work is likely to focus on cheaper and better-calibrated relevance evaluators, and on combining input checks with output checks such as verifying that the answer is supported by the kept evidence. The specific three-action scheme may matter less over time than the broader principle that retrieved context should be evaluated rather than trusted automatically.
Mise en œuvre dans le monde réel
A company help-desk bot retrieves three policy chunks for a question about parental leave, the grader finds none relevant, and the system rewrites the query and searches again instead of answering from the wrong policy.
A medical-information assistant grades retrieved passages, keeps only the sentences that mention the drug the user asked about, and discards surrounding text about other drugs before generating.
A news Q&A tool detects that its internal archive has no coverage of a very recent event and falls back to a web search for current articles.
A legal research prototype marks retrieval as ambiguous when documents are only partly relevant, and combines refined internal passages with fresh search results before answering.
Risques et garde-fous
L’optimisation d’un benchmark peut masquer des faiblesses plus larges du système.
Les coûts d’infrastructure et de maintenance sont souvent sous-estimés.
Les lacunes en matière de sécurité et d’observabilité peuvent se creuser à mesure que les systèmes deviennent plus complexes.
Feuille de route de mise en œuvre
Définissez les objectifs de latence, de qualité et de coût avant la mise en œuvre.
Benchmark dans des conditions de charge et de données réalistes.
Surveillance des instruments pour détecter les erreurs, la dérive et l'impact sur l'utilisateur.
Préparez les chemins de restauration et de réponse aux incidents avant la mise à l’échelle.
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Questions fréquemment posées
What is Corrective RAG (CRAG)?
Corrective RAG (CRAG) is a retrieval-augmented generation method that checks how relevant the retrieved documents are before answering, and takes a corrective action when retrieval looks weak, such as refining the documents, rewriting the query or searching the web. It matters because standard RAG passes whatever it retrieves to the model, and irrelevant or misleading documents are a major cause of wrong, confidently stated answers.
What core weakness of standard RAG does CRAG address?
Standard RAG passes retrieved documents to the model regardless of relevance, which can produce confident wrong answers. CRAG evaluates retrieval first.
In the original CRAG paper, what kind of model served as the retrieval evaluator?
The paper used a lightweight fine-tuned T5-large model to score document relevance.
What does CRAG do when all retrieved documents score low?
The Incorrect action discards the documents, rewrites the question into search queries and relies on refined web results.
What is the purpose of the decompose-then-recompose step?
Knowledge refinement breaks documents into small strips, scores them, and recomposes only the useful parts for the generator.
When the evaluator is unsure about relevance, which action does CRAG take?
The Ambiguous action blends both sources to hedge against uncertainty in the evaluator's judgment.
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