Even as someone with plenty of experience, this can still be a problem: I use them for stuff outside my domain, but where I can still debug the results. In my case, this means I use it for python and web frontend, where my professional experience has been iOS since 2010.
ChatGPT has, for several generations, generally made stuff that works, but the libraries it gives me are often not the most appropriate, and are sometimes obsolete or no longer functional — and precisely because web and python are hobbies for me rather than my day job, it can take me a while to spot such mistakes.
Two other things I've noticed, related in an unfortunate way:
1) Because web and python not my day job, more often than not and with increasing frequency, I ultimately discover that when I disagree with ChatGPT, the AI was right and I was wrong.
2) These specific models often struggle when my response has been "don't use $thing or $approach"; unfortunately this seems to be equally applicable regardless of if the AI knew more than me or not, so it's not got predictive power for me.
(I also use custom instruction, you YMMV)
ChatGPT has, for several generations, generally made stuff that works, but the libraries it gives me are often not the most appropriate, and are sometimes obsolete or no longer functional — and precisely because web and python are hobbies for me rather than my day job, it can take me a while to spot such mistakes.
Two other things I've noticed, related in an unfortunate way:
1) Because web and python not my day job, more often than not and with increasing frequency, I ultimately discover that when I disagree with ChatGPT, the AI was right and I was wrong.
2) These specific models often struggle when my response has been "don't use $thing or $approach"; unfortunately this seems to be equally applicable regardless of if the AI knew more than me or not, so it's not got predictive power for me.
(I also use custom instruction, you YMMV)