Lec 6 | RAG Part 4: Evaluation & Testing RAG with RAGAS + Pytest | Gen AI

Building a RAG pipeline is only half the job — how do you know it's actually working? In this lecture, we cover how to measure and test Retrieval-Augmented Generation systems so quality is backed by numbers, not guesswork. *What this covers:* Why RAG failures split into two categories — *retrieval failures* (the right information was never found) and *generation failures* (it was found, but the model didn't use it well) The four key metrics used to evaluate RAG quality: *Context Precision* — how much of what's retrieved is actually relevant *Context Recall* — whether retrieval found everything needed to answer correctly *Faithfulness* — whether the answer is grounded in the retrieved information (no hallucination) *Answer Relevancy* — whether the answer actually addresses the question How to build a small evaluation set of questions and reference answers to measure these metrics automatically Comparing a simple retrieval setup against a more advanced one, using real measured results instead of guesswork Turning evaluation into automated tests, so quality regressions get caught automatically instead of relying on someone remembering to check *Stack:* Python · OpenAI · Pinecone · RAGAS · pytest *Prerequisites:* RAG Parts 1–3 (Overview, Chunking/Embeddings, Retrieval) Part of the *Generative AI Engineering* program. #RAG #RAGAS #LLM #GenerativeAI #AIEngineering