That said, I'm not convinced there isn't something to the idea, I just don't know that that is the correct use of LLMs. I find myself wondering if from-scratch training, of a much, much smaller model trained on the original data, using LLM technology but not using one of the current monsters, might not work better. I also wonder if this might be a case where prompt engineering isn't the way to go but directly sampling the resulting model might be a better way to go. Or maybe start with GPT-2 and ask it for lists of things; in a weird sort of way, GPT-2's "spaciness" and inaccuracy is sort of advantageous for this. Asking "give me a list of names" and getting "Johongle X. Boodlesmith" would be disastrous from a modern model, but for this task is actually a win. (And I wouldn't ask GPT-2 to try to format the data, I'd probably go for just getting a list of nicely randomized-but-plausible data, and solve all the issues like "tying the references together" conventionally.)
If you have such an opinion why not share how I could communicate it better?
Basically, if thing is good it needs to still be good when tried in the real world by flawed humans, so if someone says "I tried thing and it didn't work" replying with "well maybe thing is good but you suck" isn't productive.
Now maybe I was too weird in my response to the OP but it really went into a LLMs are bad narrative.