In my experience the difficulty in this kind of task is reading the docs of a bunch of packages I haven't used in months/years and probably won't use again anytime soon, testing things manually and creating all the little harnesses to make that work without running for minutes at a time, etc.
Sure for someone who does ETL type work all day, or often enough anyway, they'd scoff, and true LLM won't really save them time. But for me who does it once in a blue moon, LLMs are great. It's still on me to determine correctness, I am simply no longer contending with the bootstrap problem of learning new packages and their syntax and common usage.
Similarly for me, my visualisation pipeline changed from "relearn matplotlib and pandas every single time" to "ask for code, fix up details later". In this case the time saving scales with how much I forgot from the docs and the last time. I need to do the review and debugging either way, so that's moot.
There's two schools of thought here: viewing LLMs as machines to replace your thinking, and viewing LLMs as a vast corpus of compressed knowledge to draw upon.
Sure for someone who does ETL type work all day, or often enough anyway, they'd scoff, and true LLM won't really save them time. But for me who does it once in a blue moon, LLMs are great. It's still on me to determine correctness, I am simply no longer contending with the bootstrap problem of learning new packages and their syntax and common usage.