It's very good at refactoring, creating boilerplate, making big changes with moderate levels of precision.
Current LLM's at least a reasonable percentage of the time still get stuck on race conditions and bugs not obvious via static analysis. If you can explain the exact source of a bug to an LLM they can get it, but if there's a seemingly obvious solution that isn't the correct one, they will try to fix things the wrong way.
It's best to use AI in areas where a lack of specificity or precision isn't a major hinderance, and all abstraction is a closed loop that won't hurt you in the future due to not knowing how it works.