A language model only knows what it read during training. Ask it about an internal document, a contract or a house procedure, and it improvises. RAG — retrieval-augmented generation — means handing it the right excerpts before it answers.
Three steps, not one
Documents are split into passages, turned into vectors and stored. When a user asks a question, the closest passages are retrieved and placed in the model's context with an instruction to stick to them.
Answer quality depends far more on chunking and retrieval than on the model itself. Most demos quietly skip that part.
What actually costs money
- Cleaning the sources: a badly OCR'd scanned PDF produces confidently wrong answers.
- Keeping it fresh: a stale index is a more dangerous source of error than having no tool at all.
- Evaluation: without a reference question set, nobody knows whether a change improved or degraded the system.
When not to bother
If the corpus fits in a few pages, put it straight in the context. RAG earns its keep once the volume exceeds what you can hand the model on every call.
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