RAG precision tuning can quietly cut retrieval accuracy by 40%, putting agentic pipelines at risk

RAG precision tuning, commonly used to optimize system performance, can paradoxically reduce retrieval accuracy by up to 40%. This trade-off poses significant risks for agentic AI pipelines that depend on accurate information retrieval. The finding highlights a critical gap: optimizing one metric can silently degrade others.