Sad reality, I’m using LLMs to review the work of our offshore programmers, and as you say: some are good, some are not - and sadly, the ones who are not are also not learning to leverage LLMs to improve their work products. Without LLM review, I’d be advocating to find some of our offshore programmers “more productive ways to apply their skills.” With four rounds of LLM review, I’m effectively rewriting (the bad ones’) code for them… every… single… pull request.
My best offshore coder figured out how to use the LLMs to review his own code, we have architecture discussions about the best things to do and never have issues with how they are done.
My worst offshore coder “doesn’t believe in using AI” and continues to submit code for merge to master branch with obvious race conditions, panic crashes, etc.
Yeah, like the rest of LLM output, I’d say 80% of an average code review is worth considering - and if that includes anything you might have otherwise missed, that’s a win compared to learning about the problem post-launch.
Sad reality, I’m using LLMs to review the work of our offshore programmers, and as you say: some are good, some are not - and sadly, the ones who are not are also not learning to leverage LLMs to improve their work products. Without LLM review, I’d be advocating to find some of our offshore programmers “more productive ways to apply their skills.” With four rounds of LLM review, I’m effectively rewriting (the bad ones’) code for them… every… single… pull request.
My best offshore coder figured out how to use the LLMs to review his own code, we have architecture discussions about the best things to do and never have issues with how they are done.
My worst offshore coder “doesn’t believe in using AI” and continues to submit code for merge to master branch with obvious race conditions, panic crashes, etc.
Yeah, regardless of what one’s stance on AI generated code is, it’s a very useful tool for catching anything you might’ve missed.
Of course it can also be great at getting stuck on red herrings lol. That’s why I gave up on Mistral
Yeah, like the rest of LLM output, I’d say 80% of an average code review is worth considering - and if that includes anything you might have otherwise missed, that’s a win compared to learning about the problem post-launch.