I shipped my fifth RAG pipeline to production in February. Top-10 recall@10 was 0.94. The team ran a demo, executive nodded, we declared victory. Two weeks later customer complaints started landing. The model was citing stale 2023 policy docs, ignoring the 2026 rewrite that ranked 4th. Somewhere between rank 4 and rank 1, the answer everyone needed was getting buried. That is the thing nobody warns you about with RAG. Your retriever can be statistically excellent at top-10 and still hand the LLM the wrong top-3. The model only reads what is in the prompt. If the right chunk is at position 7, it might as well be at position 700. The fix is a reranker layer. A second, smaller model whose only job is to re-score the top-K candidates with a query-aware comparison the first-stage retriever could not afford. Done right, it is the cheapest precision win in the entire RAG stack: 40-60% improvement on precision@3 for under 200ms of added latency.…