Everyone Builds RAG. Nobody Measures It. (RAG Evaluation Explained)

Ask any team how good their RAG system is and you'll hear "it seems pretty good." That's not a number. In this video you'll learn a simple, repeatable way to evaluate RAG: build a golden test set in an afternoon, measure retrieval and generation separately, use an LLM-as-judge without fooling yourself, and tell whether an improvement is real or just noise. Chapters 0:00 Everyone builds RAG, nobody measures it 1:17 Why nobody measures RAG (3 traps) 2:35 The two halves of RAG evaluation + the RAG triad 3:43 Building a golden dataset 6:26 Retrieval metrics: Recall@K, Precision, MRR, nDCG 9:38 Generation metrics: faithfulness, relevance, citations 11:19 LLM-as-judge, done right 13:30 Real improvement or just noise? (the one-line formula) 15:06 Error analysis 16:07 Evals in CI: regression gates 17:07 Evaluating in production 18:45 The tooling landscape 19:28 Walkthrough: one experiment, end to end 20:44 The cheat sheet 21:26 Mistakes that make evals lie 22:05 Recap The formula: SE ≈ √(p(1−p)/n); 95% interval ≈ score ± 2·SE. With 100 questions at 80%, that's ±8 points. Tools mentioned: Ragas, DeepEval, TruLens, LangSmith, Arize Phoenix, Langfuse (not sponsored). Note: experiment numbers shown in the walkthrough are illustrative. Previous video: RAG Query Processing — 9 techniques that fix bad retrieval How are you evaluating your RAG today? Tell me in the comments. #RAG #LLM #AIEngineering Tags RAG evaluation, how to evaluate RAG, retrieval augmented generation, RAG metrics, LLM as a judge, LLM evaluation, faithfulness, groundedness, answer relevance, context precision, context recall, recall at k, MRR, nDCG, RAG triad, golden dataset, Ragas, DeepEval, TruLens, LangSmith, Arize Phoenix, Langfuse, eval driven development, AI engineering, LLM ops