This is not really a slam dunk argument.

First off, this is not the kind of code I write on my end, and I don't think I'm the only one not writing scripts all day. There's a need for scripts at times in my line of work but I spend more of my time thinking about data structures, domain modelling and code architecture, and I have to think about performance as well. Might explain my bad experience with LLMs in the past.

I have actually written similar scripts in comparable amounts of times (a day for a working proof of concept that could have gone to production as-is) without LLMs. My use case was to parse JSON crash reports from a provider (undisclosable due to NDAs) to serialize it to our my company's binary format. A significant portion of that time was spent on deciding what I cared about and what JSON fields I should ignore. I could have used ChatGPT to find the command line flags for my Docker container but it didn't exist back then, and Google helped me just fine.

Assuming you had to guide the LLM throughout the process, this is not something that sounds very appealing to me. I'd rather spend time improving on my programming skills than waste that time teaching the machine stuff, even for marginal improvements in terms of speed of delivery (assuming there would be some, which I just am not convinced is the case).

On another note...

There's no need for snark, just detailing your experience with the tool serves your point better than antagonizing your audience. Your post is not enough to convince me this is useful (because the answers I've gotten from ChatGPT have been unhelpful 80% of the time), but it was enough to get me to look into AutoGen Studio which I didn't know about!