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.
A single agent with a big prompt and a pile of tools works fine until the task branches: one part needs research, another needs code execution, another needs a human sign-off before it does anything destructive. At that point you're better off with several narrow agents coordinated by explicit control flow than one agent trying to hold all of it in its head. LangGraph is built for exactly that — it models an agent system as a graph of nodes and edges over a shared state, instead of a single hidden loop.