The answer is adaptability.
In both cases for "Question Answering" it's about similarity search but there are two main orthogonal differences between RAG and Non-RAG :
-Knowing the question at the time of index building
-Higher order features : the ability to compare fetched documents with one another and refine the question
Non-RAG, aka multi-layer (non-causal) transformer with infinite context, is the more generic version, fully differentiable meaning you can use machine learning to learn how to Non-RAG better. Each layer of the transformer can use the previous layer to reason and refine the similarity search. (A causal transformer know the question at the time when it is feed the question, and can choose to focus it's attention on different part of the previously computed features of the provided documents but may benefit from having some reflection token, or better : be given the question before being presented the documents (provided you've trained it to answer it like that).)
RAG is an approximation of the generic case to make it faster and cheaper. Usually it breaks end-to-end differentiability by using external tools, so this mean that if you want to use machine learning to learn how to RAG better you will need to use some variant of Reinforcement Learning which is slower to learn things. RAG usually don't know the question at the time of index building, and documents are treated independently of each other, so no (automatic) higher order features (embeddings are fixed).
A third usual approximation, is to feed the output of RAG into Non-RAG, to hopefully get the best of both world. You can learn the Non-RAG given RAG with machine learning (if you train it with some conversations where it used RAG), but the RAG part won't improve by itself.
Non-RAG need to learn so it needs a big training dataset, but fortunately it can pick-up question answer pair in an unsupervised fashion when you feed it the whole web, and you only need a small instruction training and preference optimization dataset to shape it to your need. If performance isn't what you expect in a specific case, you can provide more specific examples and retrain the model until it gets it and you get better performance for the case you were interested in. You can improve the best case but it's hard to improve the worst case.
RAG has more control on what you feed it but content should be in a more structured way. You can prevent worst cases more easily but it's hard to improve good case.