I would phrase it as "LLMs are good at big picture stuff and bad at fine detail", or to put it another way, they're accurate, but imprecise and with low reproducibility.
I would phrase it as "LLMs are good at big picture stuff and bad at fine detail", or to put it another way, they're accurate, but imprecise and with low reproducibility.
It is my experience that it's the opposite. LLMs are very very precise but wildly inaccurate. They might give you 17 significant digits but be off by 10 orders of magnitude, to use a metaphor.
Sounds like we're in agreement, then. The 7 digits it got correct are the big picture, and the rest are the details. Are you disagreeing with my statement or with my usage of "accurate" and "precise"?
But where does that leave us when programmers treat themselves as architects with the AI doing the drudge work? As seems to be the fashion.
It then means you have 2 parties focussing on the big picture and no one focussing on the details.
I said "big picture stuff", but I guess I should have said "broad strokes". The truly correct answer is probably similar to what the model will answer, and if your problem is such that it can work with small imperfections in a solution, then the LLM helps. If the solution needs to be exactly right, then it will probably fail.
Yesterday on a whim I tried asking a local model a question about kanji that look different in different fonts despite being the same character (to the point of strokes appearing in completely different directions), and the model hallucinated imgur links to images of the characters. If imgur could work with approximate references to data maybe that would have worked.