My RAG Pipeline Was 84% Confident — And Completely Wrong.

I built a production-grade RAG system called PrecisionRAG . It combines Self-RAG and CRAG (Corrective RAG) techniques, runs on LangGraph, has hallucination checking, answer revision loops, usefulness checks, corrective re-retrieval before web search fallback - and more. Then I asked it a simple factual question and it gave me a confidently wrong answer. 84% confidence. Fully Supported. 100% useful. Completely incorrect. This is the story of how that happened, how I debugged it, and the architect