RAG Evaluation Metrics: Measuring What Actually Matters

This article discusses the importance of using specific, measurable evaluation metrics for Retrieval-Augmented Generation (RAG) systems, which enhance language model responses with retrieved context. It outlines a four-layer framework for RAG evaluation, covering retrieval quality, faithfulness, answer quality, and end-to-end task success.

Details

The article explains that RAG systems, which combine large language models (LLMs) with information retrieval, face a similar challenge to a restaurant owner receiving vague customer feedback. Just as the restaurant owner needs specific, measurable criteria to improve their food, RAG systems require a multi-layered evaluation framework to diagnose and address issues. The four-layer framework covers retrieval quality, faithfulness, answer quality, and end-to-end task success. Retrieval metrics like precision, recall, and MRR measure how well the system finds the right documents to include in the context. Faithfulness metrics assess whether the final answer matches the retrieved context, while answer quality metrics evaluate the correctness, completeness, and relevance of the generated response. The article emphasizes that problems cascade upward, so metrics are needed at every layer to fully understand and improve RAG system performance.