How to Ground a Korean AI Agent in Real Demographics with Synthetic Personas
How to Ground a Korean AI Agent in Real Demographics with Synthetic Personas
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towards data science
Your RAG system is retrieving the right documents with perfect scores — yet it still confidently returns the wrong answer. I built a 220 MB local experiment that proves the hidden failure mode almost nobody talks about: conflicting context in the same retrieval window. Two contradictory documents come back, the model picks one, and you get a fluent but incorrect response with zero warning. This article shows exactly why it happens, the three production scenarios where it silently breaks, and the tiny pipeline layer that fixes it — no extra model, no GPU, no API key required. The system behaved exactly as designed. The answer was still wrong. The post Your RAG System Retrieves the Right Data — But Still Produces Wrong Answers. Here’s Why (and How to Fix It). appeared first on Towards Data Science .
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