Building a RAG system from scratch
Retrieval-Augmented Generation (RAG) is the pattern you reach for when an LLM needs to answer questions using knowledge it wasn't trained on — your docs, your codebase, your support tickets. Instead of fine-tuning, you retrieve relevant context at query time and hand it to the model. This is a practical walkthrough of the pieces that make a RAG system actually work in production, not just in a notebook.