Retrieval-augmented generation is the standard way to make a language model answer from your data rather than only from what it was trained on. Instead of fine-tuning the model, you store your documents in a searchable form, retrieve the most relevant pieces when a question comes in, and include them in the prompt so the model answers from that context.
The appeal is practical: it keeps answers current (update the documents, not the model), it lets the system cite its sources, and it is far cheaper and faster to change than retraining. The difficulty is entirely in the retrieval, if the wrong context is fetched, the model answers confidently and wrongly. Chunking, embedding choice, ranking and evaluation are where a RAG system is actually built.