What You’ll Learn, and Why Most Explanations of RAG Miss the Point
Artificial IntelligenceIf you’re using an LLM with your own documents (or thinking about it) you’ll run into RAG, Retrieval-Augmented Generation. The premise sounds simple: feed your files to the model so it answers better. In reality it’s a pipeline of seven technical decisions, where each link determines whether your AI answers from your data or invents with confidence. This guide covers the full mechanics with the 2026 benchmarks, the concrete tools (NotebookLM, ChromaDB, LangChain), and the traps I hit building my own pipelines. From theory to a working Python pipeline in 30 lines.