A language model only knows what it absorbed during training, which can be out of date or incomplete. Retrieval-augmented generation fetches relevant documents first and feeds them to the model alongside the question, so the answer can draw on specific, current material.
This reduces guesswork and lets a system point to where an answer came from. Its quality depends on retrieving the right passages, so a weak search step produces weak answers no matter how capable the model is.