The original title is 11 words: "Your RAG Pipeline Hallucinates Because It Never Checks Its Own Work"

Short summary

Naive RAG systems hallucinate because they don't validate retrieval quality before generating answers. This tutorial shows how to build a corrective RAG pipeline using LangGraph that grades relevant docs, rewrites queries on poor matches, and reduces hallucinations from ~18% to <3%. The architecture uses a state machine with gating logic instead of relying on prompting alone.

• •Naive RAG hallucinates when retrieved docs don't answer the question but sound plausible
• •Corrective RAG adds a grading gate and query rewriting to validate retrieval before generation
• •Architecture reduces hallucination rate from 18% to <3% with only 1.5s extra latency on 15-25% of queries

Generated with AI, which can make mistakes.