RAG Retrieval Metrics Explained: Recall, Precision, MRR & NDCG

Why It Matters

Selecting and tracking the right retrieval metrics is critical to ensure RAG systems provide accurate, comprehensive and well-ranked evidence to LLMs—directly affecting answer quality, user trust and business decisions based on those outputs.

Summary

The video explains key evaluation metrics for retrieval-augmented generation (RAG), focusing on relevance, comprehensiveness and correctness of retrieved documents. It defines recall@K (how many relevant documents are found within the top K), precision@K (proportion of top-K results that are relevant), MRR (mean reciprocal rank of the first relevant result) and NDCG (how well relevant documents are ranked higher than irrelevant ones). Together these metrics let teams measure both coverage and ranking quality of retrievers. The piece emphasizes choosing appropriate metrics to verify that a RAG system is retrieving the right information for downstream answers.

Original Description

Evaluating a RAG system goes beyond just testing outputs. You need to measure retrieval quality using metrics like Recall@K, Precision@K, Mean Reciprocal Rank (MRR), and NDCG. This short gives you a quick breakdown of each metric and why they matter for building reliable RAG pipelines.

Full tutorial 👉 https://www.youtube.com/watch?v=vT-DpLvf29Q

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