Any time someone says LLMs have been a massive boost to their productivity, I have to assume that they are terrible at their job, and are using it to produce a higher volume of even more terrible work.
Those replies are a dime a dozen. Unless they’re poignant, well thought out discussions on specific failures, they’re usually from folks that have an axe to grind against LLMs or are fearful that they will be replaced.
aye. every attempt I've tried to use ChatGPT to do some moderate to advanced python scripting had it fail at something.
for the most part the code is alright... but then it references libraries that are deprecated or wrong or weren't included for some reason. example:
one time I was pulling some sample financial data from Quandl and asked it why it wasn't working right -- it mentioned that I was referencing a FED dataset that was gone. And that was true, it was old code that I pulled out of a previous project. So I asked it for a new target dataset... and it gave me an older one again.
Okay, fine, this time find me a new one -- again, was wrong. Didn't take a lot of time to find that, decided to find my own.
Go find one, then send that back to the AI... and it mangles the API key variable. An easy fix, but again, still didn't work.
The goal was to get it done quickly, to get some sample data to test a pipeline, but in practice it required help every step, and I probably could have just written it on my own from scratch in roughly the same time.
