RAG is fundamentally different and there will always be a place for it. A model's weights are inherently lossy and opaque. If a model asserts some fact, it is

RAG is fundamentally different and there will always be a place for it. A model's weights are inherently lossy and opaque. If a model asserts some fact, it is impossible to tell whether that fact was true or hallucinated just from the model, because the model has no notion of "fact" or "truth", it's just probabilities. Generating an answer solely from model weights is like asking a random person to answer a question from memory. Sure, you might get the right answer, but there's no guarantee. Using RAG is like handing them a book and asking them what the book says the answer is. With the benefit that LLMs can "read" much faster than a human. My take is that LLMs are actually much better at "reading" than they are at "writing", and RAG plays to that strength.