What is RAG Evaluation?
TL;DR
A multi-dimensional methodology to measure retrieval quality, answer relevance, and faithfulness in RAG systems before production.
RAG Evaluation: Definition & Explanation
RAG Evaluation measures the quality of Retrieval-Augmented Generation systems across multiple axes. The standard six metrics are (1) Retrieval Recall (did we fetch the right docs?), (2) Retrieval Precision (noise rate among fetched docs), (3) Faithfulness (does the answer stay grounded in retrieved content — inverse of hallucination), (4) Answer Relevance (does the answer address the query?), (5) Context Recall (is the supplied context complete?), and (6) Context Precision (are critical docs ranked at the top?). Standard frameworks include Ragas, TruLens, ARES, DeepEval, LangSmith, and LlamaIndex Evals. The play: build a Golden dataset (queries, expected answers, reference docs), then mix LLM-as-a-Judge with human review. 2026 best practice is daily automated runs against 100-500 Golden examples, ship-gating on Faithfulness >= 95%, and revisiting chunking, embeddings, and rerankers when Context Recall slips. Cohere Rerank 3, Voyage AI rerank-2, and Jina Reranker v2 commonly deliver ~30% Precision lift.