This doesn't deserve the downvotes it (currently) has. This is a completely accurate statement, simple RAG works well when questions are highly correlated with specifics chunks of documents. It does not allow an LLM to synthesize an entire corpus to an answer (e.g. a book report).

Other modes of RAG that retrieve multiple times per generation (e.g. every so-many tokens) can improve performance, and intuitively feel closer to the 'sparse attention over the entire corpus' that RAG is approximating.

However, fine-tuning on relevant, high quality, knowledge-rich question/answer pairs seems dominant, when such examples are available or can be generated.