Earlier quoted context omitted.
> I rarely trust code it generates (at least for the language I mainly work in) as i’ve seen it make some serious mistakes (eg: using keywords in the language that don’t exist) It's only a mistake from your perspective. The model just generates text based the probabilities it learned during training. In that respect, there is no such thing as "incorrect" output because the model doesn't operate at that level of abstr…
Wait, no, it's "incorrect" in the sense that you asked it to do something, and the thing it gives you doesn't accomplish the task. I asked it "what is the PS3 game where the full version of To Kill a Mockingbird is in there?" and it responded back with "The Sabateour", when the correct answer would have been "The Darkness". That is incorrect by most definitions of the word, whether or not it's a consequence of the tr…
You believe you "asked it do something," but that's just you anthropomorphizing the model and your interaction with it. Of course the AI companies encourage that perspective, but it's a factually dubious one at best.
Judging whether a model's output is "correct" involves you imposing an external context on both the prompt and response that the model typically doesn't have access to. It also typically has no ability to test its responses.
This is part of why good prompt engineering can be so important - because what you get out is a function of what you put in, and pretending that the model is a question-answering oracle only takes you so far.
Of course what the AI companies are trying to do is train and prompt the models in such a way that their output is considered "correct" from a user's perspective more often than not. In an interaction with an AI company's salespeople, you might argue about "correctness". But that's not going to help understand what's actually going on.