Honestly? I think search engines are actually the best use for LLMs. We just need them to be “explainable” and actually cite things.
Even going back to the AOL days, Ask Jeeves was awesome and a lot of us STILL write our google queries in question form when we aren’t looking for a specific factoid. And LLMs are awesome for parsing those semi-rambling queries like “I am thinking of a book. It was maybe in the early 00s? It was about a former fighter pilot turned ship captain leading the first FTL expedition and he found aliens and it ended with him and humanity fighting off an alien invasion on Earth” and can build on queries to drill down until you have the answer (Evan Currie’s Odyssey One, by the way).
Combine that with citations of what page(s) the information was pulled from and you have a PERFECT search engine.
That may be your perfect search engine, I jyst want proper boolean operators on a sesrch engine that doesn’t think it knows what I want better than I do, and doesn’t pack the results out with pages that don’t match all the criteria just for the sake of it. The sort of thing you described would be anathema to me, as I suspect my preferred option may be to you.
They are VERY VERY good at search engine work with a few caveats that we’ll eventually nail. The problem is, they’re WAY to expensive for that purpose. Single queries take tons of compute and power. Constant training on new data takes boatloads of power.
They’re the opposite of efficient; eventually, they’ll have to start charging you a subscription to search with them to stay in business.
LLM wasn’t the right tool for the job, so search engine companies made their search engines suck so bad that it was an acceptable replacement.
Honestly? I think search engines are actually the best use for LLMs. We just need them to be “explainable” and actually cite things.
Even going back to the AOL days, Ask Jeeves was awesome and a lot of us STILL write our google queries in question form when we aren’t looking for a specific factoid. And LLMs are awesome for parsing those semi-rambling queries like “I am thinking of a book. It was maybe in the early 00s? It was about a former fighter pilot turned ship captain leading the first FTL expedition and he found aliens and it ended with him and humanity fighting off an alien invasion on Earth” and can build on queries to drill down until you have the answer (Evan Currie’s Odyssey One, by the way).
Combine that with citations of what page(s) the information was pulled from and you have a PERFECT search engine.
That may be your perfect search engine, I jyst want proper boolean operators on a sesrch engine that doesn’t think it knows what I want better than I do, and doesn’t pack the results out with pages that don’t match all the criteria just for the sake of it. The sort of thing you described would be anathema to me, as I suspect my preferred option may be to you.
They are VERY VERY good at search engine work with a few caveats that we’ll eventually nail. The problem is, they’re WAY to expensive for that purpose. Single queries take tons of compute and power. Constant training on new data takes boatloads of power.
They’re the opposite of efficient; eventually, they’ll have to start charging you a subscription to search with them to stay in business.
So my company said they might use it to improve confluence search, I was like fuck yeah! Finally a good use.
But to be fair, that’s mostly because confluence search sucks to begin with.
You’re describing Bing Chat.
And google gemini (?) and kagi’s LLM and all the other ones.