RAG Explained: The "Open-Book Exam" That Makes LLMs Smarter

Ever wonder how AI can accurately answer questions using YOUR private documents without making things up? Welcome to Episode 6 of our AI Foundations series, where we unpack Retrieval-Augmented Generation (RAG)! Large Language Models (LLMs) only know two things: their frozen training data and what you fit into their context window right now. Ask about last week’s company memo, and they’ll either say "I don't know" or hallucinate a plausible answer. RAG fixes this without expensive retraining—giving the model an "open-book exam" so it checks the actual textbook page before writing an answer. In this video, we break down: 1️⃣ The 6-Step RAG Pipeline (Chunking, Embedding, Vector Search, & Context Stuffing) 2️⃣ Chunking & Overlap — Why size matters (and the Pizza Slicing rule) 3️⃣ RAG vs. Pasting Everything vs. Fine-Tuning — Speed, cost, and freshness trade-offs [7-9]