I feel like all of the interesting stuff when it comes to search+LLM is in real hybridization that's still a few steps out from what's happening right now in terms of productization.
For example:
(a) Addressing the recency problem by making a model generate instructions to a browser as output token stream, so it can, well, Google for you and incorporate latest info into output by adding it to the "internal prompt".
(b) Doing something similar where you teach models how to drive computational engines to make actual, computed facts and figures, not guess them from statistics over training data.
(c) Maybe running a traditional search and then using LLMs to summarize over them.
etc. etc.
It feels increasingly clear that "complete prompt from a stale training data set" is a neat trick but doesn't solve any particular problem well enough. This also seems intuitively true: If you ask your friend a difficult question, you often don't expect them to be able to answer it on the spot. You probably expect them to have relevant experience and a rough understanding of the topics and the tools to look up or figure out the rest. Similarly, it feels intuitively wrong to expect a LLM to come ready-made with all knowledge baked in. It feels more like we'd want something with the working knowledge/method to plan and execute fact discovery and computation.
It's fun to play around a bit with ChatGPT yourself and try to approximate what a more complex pipeline might be. For example, instead of asking it to answer a question, ask it to re-state the question or make a list of questions prompted by the original question, then let it "contemplate" those or propose a method for how to go after answering them, etc. It runs aground pretty often, but sometimes the results take you a lot further than the direct approach.