98. RAG: Give Your AI Access to Your Documents

You ask ChatGPT about your company's internal policies. It makes something up. It sounds confident. It's wrong. That's the hallucination problem. LLMs generate text based on what they learned during training. If the answer wasn't in the training data, they fabricate one that sounds plausible. RAG (Retrieval Augmented Generation) fixes this. Before generating, the system retrieves relevant documents from your own knowledge base. The LLM reads those documents and generates an answer grounded in real content. Your documents. Your data. Accurate answers. What You'll Learn Here Why RAG beats fine-tuning for knowledge-heavy tasks The complete RAG pipeline: chunk, embed, retrieve, generate Chunking strategies that actually work Building RAG from scratch with sentence-transformers and a local LLM Building RAG with LangChain for real projects Evaluating RAG: what good looks like and what breaks it Common failure modes and how to fix them RAG vs Fine-Tuning: When