That's a good use case, and I can easily imagine that you get good results from it because (1) it is for a domain that you are already familiar with and (2) you are able to check that the results that you are getting are correct and (3) the domain that you are leveraging (coding expertise) is one that chatgpt has ample input for.

Now imagine you are using it for a domain that you are not familiar with, or one for which you can't check the output or that chatgpt has little input for.

If either of those is true the output will be just as good looking and you would be in a much more difficult situation to make good use of it, but you might be tempted to use it anyway. A very large fraction of the use cases for these tools that I have come across professionally so far are of the latter variety, the minority of the former.

And taking all of the considerations into account:

- how sure are you that that code is bug free?

- Do you mean that it seems to work?

- Do you mean that it compiles?

- How broad is the range of inputs that you have given it to ascertain this?

- Have you had the code reviewed by a competent programmer (assuming code review is a requirement)?

- Does it pass a set of pre-defined tests (part of requirement analysis)?

- Is the code quality such that it is long term maintainable?