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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

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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.

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