RAG (retrieval-augmented generation)
A pattern where relevant documents are retrieved from your own data and inserted into the prompt before the model answers.
RAG is how you make a general model answer questions about your specific content - a help centre, a research repository, a product spec library - without retraining anything.
The quality of a RAG system is mostly the quality of its retrieval. If the wrong chunks are fetched, no amount of prompt tuning fixes the answer.