kind of bizarre. the best use, arguably, of an LLM is as a very fast search index for common information. you not using it and opting to parse the information by hand is essentially the same thing, perhaps slower. you might be fortunate to be in a position where nobody is relying on your velocity to ship products at competitive speed.
the best use, arguably, of an LLM is as a very fast search index for common information
I'd counter forming a rebuttal against a search result would be fairly trivial. I.e an AI summary for "are oranges poisonous" would say "Moms Against Big Citrus says oranges are poisonous, but the Association of Orange Groves says they are not." When used for search, LLMs are a source for sources instead of the source of truth itself. The user decides which source to believe instead of deciding to believe the LLM itself.
you might be fortunate to be in a position where nobody is relying on your velocity to ship products at competitive speed.
Admittedly the biggest drawback. AI is pretty terrible at my profession (electrical engineering, regardless of recent demos that are horrendous when examined under a microscope), but the software engineers I know haven't hand written industry code in months.
also, I'm curious what the last generation of models you used is and to what extent. blanket-level "hallucination" isn't really something I run into often anymore. inconsistencies or obfuscation, sure, but that's not the same.
Professionally, I'm forced to use CoPilot, which is so eager to please me that it's useless, and Claude which is fairly good at writing code but hallucinates data when I ask it to organize my notes.
Personally, I'm using Qwen, Phi, etc. to squeeze as much performance out of constrained hardware as possible. They're pretty good at searching my personal files which you mentioned in a previous comment.
kind of bizarre. the best use, arguably, of an LLM is as a very fast search index for common information. you not using it and opting to parse the information by hand is essentially the same thing, perhaps slower. you might be fortunate to be in a position where nobody is relying on your velocity to ship products at competitive speed.
I'd counter forming a rebuttal against a search result would be fairly trivial. I.e an AI summary for "are oranges poisonous" would say "Moms Against Big Citrus says oranges are poisonous, but the Association of Orange Groves says they are not." When used for search, LLMs are a source for sources instead of the source of truth itself. The user decides which source to believe instead of deciding to believe the LLM itself.
Admittedly the biggest drawback. AI is pretty terrible at my profession (electrical engineering, regardless of recent demos that are horrendous when examined under a microscope), but the software engineers I know haven't hand written industry code in months.
also, I'm curious what the last generation of models you used is and to what extent. blanket-level "hallucination" isn't really something I run into often anymore. inconsistencies or obfuscation, sure, but that's not the same.
Professionally, I'm forced to use CoPilot, which is so eager to please me that it's useless, and Claude which is fairly good at writing code but hallucinates data when I ask it to organize my notes.
Personally, I'm using Qwen, Phi, etc. to squeeze as much performance out of constrained hardware as possible. They're pretty good at searching my personal files which you mentioned in a previous comment.
edit: typos