Corrective Retrieval Augmented Generation
by Beihang University / University of Illinois Urbana-Champaign / Microsoft · free · Last verified 2026-03-17
Corrective Retrieval Augmented Generation (CRAG) is an AI framework that enhances standard RAG by adding a self-correction layer. It uses a lightweight retrieval evaluator to score the relevance of retrieved documents. If documents are deemed irrelevant or ambiguous, CRAG triggers corrective actions like web searches to improve the knowledge source before generation.
https://arxiv.org/abs/2401.15884 ↗B
B—Above Average
Adoption: BQuality: AFreshness: B+Citations: BEngagement: F
Specifications
- License
- Open Access
- Pricing
- free
- Capabilities
- self-correcting-retrieval, lightweight-retrieval-evaluation, dynamic-data-sourcing, web-search-fallback, ambiguity-detection, relevance-scoring, knowledge-refinement, improved-robustness-against-hallucinations
- Integrations
- [object Object], [object Object], [object Object]
- Use Cases
- [object Object], [object Object], [object Object], [object Object]
- API Available
- No
- Tags
- rag, corrective-rag, web-search, retrieval-evaluator, robustness, llm, information-retrieval, self-correcting-ai, knowledge-augmentation, fact-checking
- Added
- 2026-03-17
- Completeness
- 0.95%
Index Score
61Adoption
68
Quality
84
Freshness
72
Citations
68
Engagement
0