CRAG is the Corrective Retrieval-Augmented Generation framework that evaluates retrieval quality and applies corrective actions when evidence is weak - it aims to prevent low-quality retrieval from propagating into poor final answers.

What Is CRAG?

• Definition: RAG architecture with explicit retrieval quality assessment and correction paths.
• Correction Actions: Can trigger web fallback, query refinement, filtering, or answer abstention.
• Quality Estimation: Uses confidence signals to judge whether retrieved evidence is sufficient.
• Pipeline Goal: Improve robustness when initial retriever results are incomplete or noisy.

Why CRAG Matters

• Failure Containment: Stops weak retrieval sets from driving confident but wrong answers.
• Robustness: Adds resilience against domain drift and sparse-corpus edge cases.
• Safety Benefit: Supports abstain-or-retry behavior when evidence quality is low.
• Answer Reliability: Corrective loops increase chance of evidence-backed final outputs.
• Operational Visibility: Quality scores provide diagnostics for retriever health monitoring.

How It Is Used in Practice

• Quality Classifier: Score retrieval bundles before generation proceeds.
• Correction Policy: Route low-confidence cases into refinement or fallback pipelines.
• Outcome Logging: Track correction triggers and downstream answer accuracy for tuning.

CRAG is a robust control pattern for handling retrieval uncertainty - CRAG improves reliability by adding explicit quality checks and corrective actions.