A few months ago, I built a chatbot that sounded very smart… Until it started confidently giving completely wrong answers. It hallucinated: Product details that didn’t exist Outdated policies Even made-up information That’s when I realized something important: LLMs are great at reasoning But terrible at remembering accurate, up-to-date facts That’s exactly where RAG (Retrieval-Augmented Generation) comes in. What is a RAG System (In Simple Terms)? Instead of relying on memory, a RAG system: Retrieves relevant data Feeds it to the model Generates an answer based on real context Think of it like: Closed-book exam → LLM alone Open-book exam → RAG system The Core Architecture A basic RAG pipeline looks like this: Documents → Chunking → Embeddings → Vector DB User Query → Retrieval → LLM → Answer The key idea: The model doesn’t guess — it looks things up first Minimal Working Example (Python) Let’s build a simple version step-by-step.…