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I think it’s pretty clear that LLMs can process a document/article/web page faster than any human in order to answer a given question. (And it can be parallelized across multiple pages at once too).

The main hard part of searching isn’t formulating queries to write in the Google search bar, it’s clicking on links, and reading/skimming until you find the specific answer you want.

Getting one sentence direct answers is a much superior UX compared to getting 10 links you have to read through yourself.




Only if it is reliably correct.

Google does offer an AI summary for factual searches and I ignore it as it often hallucinates. Perplexity has the same problem. OpenAI would need to solve that for this to be truly useful


IME Google's summary is not actually hallucinating, the problem is they are forcing it to quote the search results, but they're surfacing bad/irrelevant search results because Google's actual search hasn't worked in years. It's a RAG failure.

For instance I searched for the number to dial to set call forwarding on carrier X the other day, and it gave wrong results because it returned carrier Y.


This is why my most used LLM after code suggestions is Bing. I like that it has lots of references for the things I ask it to double check and read more, but at the same time it can help me dig deeper into a subject rapidly and better formulate the exact question I'm trying to ask and give me a link to the actual data it's getting it's info from.


Agreed, hallucinations can be pretty bad and can hurt trust a great deal.


> Getting one sentence direct answers is a much superior UX compared to getting 10 links you have to read through yourself.

If we assume that people want a 'direct answer', then of course a direct answer is better. But maybe some of us don't want a 'direct answer'? I want to know who's saying what, and in which context, so I can draw my own conclusions.




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