(If it's a _production_ issue, then you should talk to whoever runs your databases and ask them to look at their diagnostics; most DBMs will have a slow query log, for a start. You could also enable logging for a sample of traffic. There are all sorts of approaches likely to be more productive than _guessing_.)
While with GPT you can get an answer in 10 seconds, and then potentially try out the query in the database yourself to see if it works or not. If it worked for him so far, it must've worked accurately enough.
I would see this some sort of niche solution although OP seemed to indicate it's a recurrent thing they do.
I have used ChatGPT for thousands of things, which are on the scale of like this, although I would mostly use if it's potentially an ORM I don't know anything about in a language I don't have experience with, e.g. to see if does some sort of JOIN underneath or does an IN query.
If there was a performance issue to debug, then best case is that the query was problematic, and then when I run the GPTs generated query I will see that it was slow, so that's a signal to investigate it further.
I mean, I could consult my Tarot cards for insight on how to proceed with debugging the problem, that would not be useless. Same for Oblique Strategies. But in this case, I already know how to debug the problem, which is to change the logging settings on the ORM.
But now it's so easy with GPT to get the queries exactly as my use-case needs them. And it's not just SQL queries, it's anything data querying related, like Google Sheets, Excel formulas or otherwise. There are so many niche use-cases there which it can handle so well.
And I use different SQL implementations like Postgres and MySQL and it's even able to decipher so well between the nuances of those. I could never reproduce productivity like that. Because there's many nuances between MySQL and Postgres in certain cases.
So I have quite good trust for it to understand SQL, and I can immediately verify that the SQL query works as I expect it to work, and I can intuitively also understand if it's wrong or not. But I actually haven't seen it be really wrong in terms of SQL, it's always been me putting in a bad prompt.
Previously when I had a more complicated query I used to remember a typical experience where
1. I tried to Google some examples others have done.
2. Found some answers/solutions, but they just had one bit missing what I needed, or some bit was a bit different and I couldn't extrapolate for my case.
3. I ended up doing many bad queries, bad logic, bad performing logic because I couldn't figure out a way how to solve it with SQL. I ended up making more queries and using more code.
This is low hanging fruit. ChatGPT can do this, and also easy to verify it got it right.