[dkjaudyeqooe]

I don't know exactly how you use it, but this isn't my experience at all. If you ask a LLM anything too specific, that isn't obvious and a common issue/discussion ( something that I almost never need to do), it just makes up nonsense to fill the space.

Equally, if you ask it general questions it misses information and is almost always incomplete, leaving out slightly more obscure elements. Again, I need comprehensive answers, I can come up with incomplete ones myself.

What's really obvious to me when I use it is that it's a LLM trained on pre-existing text, that really comes through in the character of its answers and its errors.

I've very glad others find them useful and productive, but for me they're disappointing given how I want to use them.

[orzig]

That's fair, it might not be for you. In 'old school ML', for a binary classifier, there's the concept of Precision (% of Predicted Positive that's ACTUALLY Positive) and Recall (% of ACTUALLY Positive that's Predicted to be Positive).

It sounds like you want perfect Precision (no errors on specific Qs) and perfect Recall (comprehensive on general Qs). You're right that no model of any type has ever achieved that on any large real-world data, so if that's truly the threshold for useful in your use cases, they won't make sense.

[dkjaudyeqooe]

I just want something useful. I'm not talking perfection, I'm talking about answers which are not fit for purpose. 80% of the time the answers are just not useful.

How are you supposed to use LLMs if the answers they give are not salvageable with less work than answering the question yourself using search?

Again, for some people it might be fine, for technical work, LLMs don't seem to cut it.