At its most basic perhaps. But the LLM has an enormous semantic corpus embedded in its model that augments the retrieved document. The retrieved document in a w

At its most basic perhaps. But the LLM has an enormous semantic corpus embedded in its model that augments the retrieved document. The retrieved document in a way cements the context better to help prevent wandering into hallucinations. So the LLM would indeed be able to summarize the retrieved document, but also synthesize it with other “knowledge” embedded in its model. But the more important thing is you can interrogate the LLM to ask it the specific questions you have based on what it has said and your goals. Contrast this to an information retrieval based methods where you read the article hoping your questions are answered, and when they aren’t you are stuck digging through less and less relevant results or refining a search string hoping to find the right incantation that tweaks the index in the right way, sifting through documents that may contain the kernel of information somewhere if it wasn’t SEO’ed out of existence. This is a really unnatural way of discovering information - the natural way, say with a teacher, is to be told background, ask questions, and iterate to understanding. This is how chat based LLMs work. However with RAG you can ground them more concretely, as their model is a massive mishmash of everything that may or may not embed the information sought, but it’s also mixed in with everything else trained. You can bring in factual information into context that may not have even been trained. However the facts are a small aspect of knowledge - the overall semantics in the total corpus supports the facts in adjacent areas.