Your RAG system retrieves perfect documents but still produces wrong answers. A 220 MB local experiment reveals the hidden failure mode: conflicting context in the same retrieval window. When two contradictory documents return, the model picks one and delivers a fluent but incorrect response with zero warning.

This article demonstrates exactly why it happens through three production scenarios: financial restatements where audited figures override preliminary ones, policy updates that arrive too late, and API documentation that never gets deprecated. The pipeline has no layer that detects or handles these conflicts, and your retrieval metrics won't catch them.

The solution is a tiny pipeline layer that detects conflicts before they reach the QA model. No extra model, no GPU, no API key required. The experiment proves that even with perfect retrieval scores, your RAG system can silently choose the wrong answer when presented with contradictory information.