Corrective RAG: Making LLMs Trustworthy Through Feedback Loops

TL;DR

Corrective RAG introduces a self-verification step in retrieval-augmented generation systems. It uses a second LLM pass to validate that the generated output is actually supported by the retrieved sources — dramatically reducing hallucinations in domains where precision matters.

What Is Corrective RAG?

Corrective RAG (CRAG) is a variation of the standard Retrieval-Augmented Generation architecture that adds a feedback or correction loop to verify the model's own responses against the sources retrieved.

When and Why to Use Corrective RAG

• Legal research: validating case references and precedents.
• Financial and compliance tools: confirming that advice aligns with verified data.
• Healthcare/medical apps: ensuring generated content matches verified studies.
• Enterprise knowledge bases: filtering outdated or irrelevant docs automatically.

Implementation in N8N

• Retrieve documents from your vector database.
• Generate an initial answer using ChatGPT node.
• Verify using a second ChatGPT node to check if answer is fully supported.
• Add an If node: if verification = unsupported, route to another retrieval with expanded query.
• Optional: Filter low-scoring documents via LLM evaluation before regenerating.

Implementation Patterns

• Partition retrieved text into knowledge strips and score each for relevance.
• Use a smaller retriever evaluator (LLM or embedding model) to grade documents.
• Trigger secondary retrieval only when confidence falls below threshold.
• Optionally integrate web search APIs (Tavily or Bing Search) when no relevant context found.

Strengths & Weaknesses

Strengths: Significantly lowers hallucination rate in high-stakes use cases, explicitly verifies factual grounding using retrieved data, compatible with any standard RAG pipeline. Weaknesses: Increased latency from extra API calls and loops, more complex control flow, verifier accuracy depends on prompt design and LLM version.