When I first started learning Retrieval-Augmented Generation (RAG), I thought the hardest part would be understanding Large Language Models. I was wrong. I thought I would spend most of my time: Choosing the best LLM Writing better prompts Tweaking model parameters Instead, I ended up spending most of my time thinking about search . And that's when something clicked: RAG isn't primarily an AI problem. It's a search problem. Let me explain. The Mental Model Most Beginners Have When we first interact with ChatGPT or any AI assistant, we imagine something like this: Question ↓ AI ↓ Answer Enter fullscreen mode Exit fullscreen mode Simple, right? Ask a question. Get an answer. But once you start building applications with your own data, this model breaks. Fast. My First "Wait... Why Is This Wrong?" Moment Imagine asking an AI: "What happened in yesterday's IPL match?" Or: "What's the latest version of this framework?" Or: "What does page 42 of this PDF say?" The model might answer confidently. The problem?…