Your RAG Isn't Hallucinating. Your Retrieval Is Lying.
SMRTR summary
When a RAG system gives a wrong answer, most teams blame the AI model and start rewriting prompts — but the real problem is usually the retrieval layer that feeds the model bad information. Poor document chunking, mismatched embeddings, and missing rerankers mean the right answer never reaches the model at all. Measuring retrieval quality using Recall@k and MRR scores can catch these failures early, before they reach users.
SMRTR provides this summary for quick context. The original article belongs to Hacker Noon.
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