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.
Comments
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.