Corrective RAG Pipeline Cuts AI Hallucinations from 18% to Under 3%

A common failure in standard RAG-based chatbots occurs when a language model generates confident but incorrect answers because the retrieved documents never actually address the user's question. The proposed fix, called corrective RAG, adds a relevance-grading step that evaluates retrieved documents before generation and rewrites the query if the results are poor. Built using LangGraph, the pipeline reduces hallucinated citations from roughly 18% to under 3% in internal evaluations. The added grading and retry logic introduces approximately 1.5 seconds of extra latency, but only triggers on the 15–25% of queries where retrieval quality is low. Rather than generating a misleading answer, the system either retries with a rewritten query or flags the response as low-confidence when reliable context cannot be found.