04 of 12 - Retrieval-Augmented Generation (RAG) Explained

RAG is the most-misunderstood piece of production AI. It's not "stuff a model with your docs" — it's a six-stage pipeline where every stage has a failure mode, a metric, and a cost. This video walks the pipeline end to end, and the eval layer that tells you which stage is actually broken. The model didn't hallucinate. The retrieval did — and you'll know the difference after this one. 📌 WHAT YOU'LL LEARN • Why RAG, why not fine-tuning, and why long context doesn't replace it • The full pipeline: chunking → metadata → embedding → vector store → hybrid search → reranking • Chunking and metadata — the design decisions that decide your recall • Hybrid search + reranking: the two steps that move RAG from demo to production • Grounding and citations — the part that makes an answer defensible • The eval split: retrieval metrics vs. generation metrics, LLM-as-judge, probing, RAGAS • An eval set in CI, plus the production checklist for the ways RAG fails If you're building RAG — or debugging RAG that's lying — this is the map. 📚 THE SERIES "Building Production AI, LLM & Agentic Apps" is a 12-part series that walks the entire stack, one layer at a time. By the end you'll be able to name every layer of a production AI app, build the AI features (prompts, structured outputs, retrieval, agents), build the app around them the way an enterprise expects, and explain it to the people who sign off. Next up — Video 5: Agentic AI & Tool Calling.