Retrieval-Augmented Generation, or RAG, solves a specific problem: large language models do not know your business. They were not trained on your documentation, your support tickets, or your product catalog, and fine-tuning a model on that data is usually the wrong tool for keeping it current.
How it actually works
At its core, RAG is three steps: turn your content into embeddings and store them in a vector database, retrieve the most relevant chunks for a given question, and pass those chunks to the model as context alongside the question itself. The model then answers using what it was just given, not just what it memorized during training.
Where it earns its complexity
RAG is worth building when your knowledge base changes often, when answers need to cite something real, or when you cannot afford the model confidently making things up. Internal support tools, documentation assistants, and search-heavy products are the common cases.
Where it is overkill
If your use case is a handful of static facts, a well-written prompt might be all you need. RAG adds real infrastructure, including chunking strategy, embedding pipeline, and retrieval tuning, and that infrastructure needs maintenance.
The part most teams underestimate
Retrieval quality matters more than model choice. A great model given the wrong context still gives a wrong answer. Most of the effort in a good RAG system goes into chunking, metadata, and retrieval, not the generation step itself.