When I apply LLMs to domains where I'm already an expert, I find it particularly lacking when I want it to do deep, difficult, novel work that requires precision. On the surface, the output looks pretty amazing at first. But when I turn a critical eye to every detail, I end up finding a lot of flawed "thinking", and the lengthy process of fully understanding what it generated and cleaning it up to my standards makes me question the entire value proposition. However, I find it does a great job being a low-level automaton sort of assistant.
For instance, in the domain of software engineering: I would not trust it to implement a major architectural change, or a groundbreaking, complex new feature. I would trust it more (but not completely) on something like a refactoring that may touch thousands of lines in a fairly mechanistic way, but that was a little too-complicated for simpler tools likes regexes. While that's kind of a nifty use, I think it's fair to say that non-LLM software purpose-built for such tasks can probably do the same thing more effectively for less real cost (meaning the currently-subsidized real cost of all the training and inference power burn, etc)
For instance, in the domain of software engineering: I would not trust it to implement a major architectural change, or a groundbreaking, complex new feature. I would trust it more (but not completely) on something like a refactoring that may touch thousands of lines in a fairly mechanistic way, but that was a little too-complicated for simpler tools likes regexes. While that's kind of a nifty use, I think it's fair to say that non-LLM software purpose-built for such tasks can probably do the same thing more effectively for less real cost (meaning the currently-subsidized real cost of all the training and inference power burn, etc)