If it's such a mind numbing problem it's easy to check it though, and the checking you do after the LLM will be much smaller than you writing every field (implicitly "checking" it when you write it).
Obviously if it's anything even minorly complex you can't trust the LLM hasn't found a new way to fool you.
True. The counterpoint being that back in the days, they could have decided to write a parser if the data was structured and they would have then learnt things that they will never learn by relying on AI.
For a junior in the learning phase that can be useful time spent. Then again, I agree that at times certain menial code tasks are not worth doing and llms are helpful.
It's a bit like a kid not spending time memorizing their time tables since they can use a calculator. They are less likely to become a great mathematician.
This is exactly it. There wasn't any complex logic. Just making sure the right fields were mapped, some renaming, and sometimes some more complex joins depending on the incoming data source and how it was represented (say multiple duplicate rows or a single field with comma delimited id's from somewhere else). I would have much rather scanned the LLM output line by line (and most would be simple, not very indented) then hand writing from scratch. I do admit it would take some time to review and cross reference, but I have no doubt it would have been a fraction of the time and effort.