The number-one frustration, cited by 45% of respondents, is dealing with “AI solutions that are almost right, but not quite,” which often makes debugging more time-consuming. In fact, 66% of developers say they are spending more time fixing “almost-right” AI-generated code.
Not surprising at all. When you write code, you’re actually thinking about it. And that’s valuable context when you’re debugging. When you just blindly follow snippets you got from some random other place, you’re not thinking about it and you don’t have that context.
So it’s easy to see how this could lead to a net productivity loss. Spend more time writing it yourself and less time debugging, or let something else write it for you quickly, but spend a lot of time debugging. And on top of it all, no consideration of edge cases and valuable design requirement context can also get lost too.
I use LLMs mainly for “editing text”. Like if I have to refactor 100 lines of code and it can’t be easily done with a regexp replace I will use LLM to do it. When I have to actually modify some logic I find it easier and faster to just do it than to explain what needs to be done to a LLM and carefully check its response for subtle bugs.
Maybe I’m misunderstanding, but are you saying that you use LLMs as refactoring tools, so things like to move code around, rename stuff, extract functions, and make changes that don’t change the logic?
Or is it something else? Because as far as I know, LLMs are pretty bad at not making random changes, even if told to just reorder stuff, plus we have a lot of deterministic tools for that job, so I guess you probably mean something else. Honest question.
