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The single biggest use I've found for LLMs has been search or lookup functionality. Instead of a carefully crafted google search + "reddit" I can just ask a que

The single biggest use I've found for LLMs has been search or lookup functionality. Instead of a carefully crafted google search + "reddit" I can just ask a question and get a specific answer with references. Some times it's wrong, generally not. Runner up is copilot integration in the IDE that I ask to transform data or reorganize lines of code. It's grunt work and having smart automation around it is helpful. Anything requiring "thinking" generally falls over pretty quickly. For example I've found broader feature code generation has generally been crap or worse and often using APIs that just don't exist. Catch is that LLMs have been sold as thinking machines that will replace people and VCs have thrown all their money into that fire. P.S. VIM has a growning following and vscode user counts are astronomical, so I'm not sure your point stands.

nunez 2y ago
> The single biggest use I've found for LLMs has been search or lookup functionality. Instead of a carefully crafted google search + "reddit" I can just ask a question and get a specific answer with references. Some times it's wrong, generally not. This is the use case that I struggle with the most. Let's say that I want to know how to reheat a baguette. If I throw that into ChatGPT, it will give me an answer based on...who actually knows? It could be from the set of responses it found most often in its training. It could be from reddit. It could be mashing these up into something completely incorrect. Perplexity and Kagi Assistant are better in that they give you some of the sources they used in generating an answer, but now we're back to a search engine with extra steps. With a search engine, I can go through each source and know (a) that the answers aren't altered and (b) who wrote the answer and whether I should trust them. To wit: I once used ChatGPT 4 to find me good restaurants in Houston based on /r/houston subreddit, with sources. (This is the best way to find new restaurants in my opinion.) It gave me five recommendations that (a) were not the best recs and (b) had fake Reddit URLs for sources. How can anyone trust that?

nunez 2y ago

> The single biggest use I've found for LLMs has been search or lookup functionality. Instead of a carefully crafted google search + "reddit" I can just ask a question and get a specific answer with references. Some times it's wrong, generally not. This is the use case that I struggle with the most. Let's say that I want to know how to reheat a baguette. If I throw that into ChatGPT, it will give me an answer based on...who actually knows? It could be from the set of responses it found most often in its training. It could be from reddit. It could be mashing these up into something completely incorrect. Perplexity and Kagi Assistant are better in that they give you some of the sources they used in generating an answer, but now we're back to a search engine with extra steps. With a search engine, I can go through each source and know (a) that the answers aren't altered and (b) who wrote the answer and whether I should trust them. To wit: I once used ChatGPT 4 to find me good restaurants in Houston based on /r/houston subreddit, with sources. (This is the best way to find new restaurants in my opinion.) It gave me five recommendations that (a) were not the best recs and (b) had fake Reddit URLs for sources. How can anyone trust that?