Problem I'm seeing with RAG-like solutions is you're presenting the LLM with some similar word blocks and hoping it covers all your bases, but won't pick up on 2nd order relationships that would be important for a more complete picture. Which isn't a whole lot better than just showing the search results to the user and might actually introduce faulty information. I think we either need a different model type that can serve as the "memory" or consider options like pre-training to ingest the data that come at the cost of needing much larger servers to perform the operation.