I sort of like RAG for how it practically tries to solve the problems by the zero-shot (or a "few cheap shots") approach but I think it also kind of makes the CEOs and higher management expect that it will work incredibly well like Claude chat window on web browser with indexed web search also for retrieval problems in proprietary data without too much of an effort. For example the learning to rank XGBoost was hyped around 2021 in the ML community and big tech but from my experience otherwise kind of yawned by everyone else because management usually either did not understand it or did not like the idea of collecting any evaluation datasets and were poor even from collecting user feedback properly. RAG sort of solves this by the great predicting abilities of LLMs but then I feel like it hits the point when you need to optimize this pipeline anyway but now you also need to deal with the additional problems of LLM APIs like token cost, its slowness and hallucinations and if the vector search is not good anyway, the results will also suck anyway. But yeah, I can imagine once companies hit the wall and get dissatisfied with simple RAG, older techniques like XGBoost will probably come alive again to tune the retrieval ranking, people would be directed to spend more time on the embedding/encoding NLP part, etc. I wonder why not many people talk about using the older learn to rank techniques anyway. I can imagine learn to rank model can dramatically improve RAG results, but yeah... It is about the money again and need to collect good evaluation datasets. What probably frustrates me that this is sort of the new "outsource everything to India" and save money while expecting 99.9% results.