To some degree. The amount of data that will be brought into search solutions will be enormous, seems like a good time to try to reimagine what that process might look like
Also this is search for LLM not for humans so optimal solution will be different. Or even with models it is not that hard to imagine that Mistral-8b will need different results than GPT4 which has 1.76 trillion parameters.
I think this is premature optimisation. LLMs are the general tool here - in principle we should try first to adjust LLMs to search instead of doing it the other way around.
But really I think that LLMs should use search as just one of their tools - just like humans do. I would call it Tool Augmented Generation. And also be able to reason through many hops. A good system answer the question _What is the 10th Fibonacci number?_ by looking up the definition in wikipedia, writing code for computing the sequence, testing and debugging it and executing it to compute the 10th number.