Many RAG failures get blamed on "the LLM" when the generator never had a chance: the passage it needed was not in the context, or it was buried under near-duplicates. Retrieval deserves its own evaluation. It is also easy to get wrong, because the score depends on what you call "relevant", how you built the questions, which k you chose and how many questions you asked.

This article covers the whole chain as it stands in late 2026: labelled question sets (including synthetic ones and their traps), the metrics and where each misleads, statistical noise, lexical versus paraphrased questions, evaluating chunking, hybrid search and rerankers, LLM-as-judge and its documented biases, end-to-end metrics, current tools and benchmarks, and a recipe you can apply this week.

Numbers of my own come from small, reproducible experiments in my public lab repo, applied-llm-lab .…