Key takeaways
- RAG fits when answers must come from your documents
- It improves relevance but does not guarantee correctness
- Evaluate retrieval and answers on real questions before launch
What RAG actually does
A language model knows a lot in general and nothing specific about your business. RAG fixes that at question time: the system searches your documents for relevant passages and hands them to the model with the question. The model then answers using that material, ideally citing it.
Because your content stays in your own store, updating an answer means updating a document, not retraining a model.
Where it fits
Internal policy and procedure search, product and technical documentation, support knowledge bases, and research across a large document set are all good fits. The common thread is a lot of text, frequent questions, and answers that need to be traceable.
Where it does not
If your knowledge fits comfortably on a page, put it in the prompt. If the task needs exact calculation or live data, you want a tool call or a database query, not retrieval. If your documents are out of date or contradict each other, RAG will faithfully surface the mess.
The limits you should plan for
RAG reduces wrong answers but does not remove them. Retrieval can miss the right passage. The model can misread a good one. Permissions matter too: a system should only retrieve what the asker is allowed to see.
Good systems say 'I could not find that' rather than guessing, show their sources, and are tested on a set of real questions before launch and again after content changes.