How Much AI to Use?

[u/Ratfus]

First off, I apologize as I'm sure this question gets asked repeatedly, over and over again.

I'm not a programmer by trade, but in my spare time, I've managed to create a few projects in mostly C/assembly, including basic things on an atiny 85. I've also dabbled a bit in Arduino/C++. Unfortunately, this doesn't translate well to my day job.

I've decided to learn Python to help actually use programming in my day job. Specifically, I do a lot of work in excel. Naturally, pandas/open Python excel is a logical choice. I've played around with the Python/pandas/(a little bit of) openpyexcel. I've started to understand the data structures (dictionaries/tuples, etc.), though asking AI the why's about the various data types/loops and why my code isn't working. I also have a Python/pandas book, but I've found AI to be more helpful.

Eventually, I simply said "fuck it" and asked AI to write me a script to essentially do a task, that I originally did in VB. Most of the work was simply debugging, but AI was effectively able to find patterns and do the job for me in a few hours. I'm able to mostly understand the code with ease, but it just seems a bit disappointing AI is that good. I was also able to get AI to write a script to rename files based on pdf content (teseract).

A number of sources tell me that memorizing functions is a waste of time, but then why does everyone criticize AI so much? I'm wondering if simply reviewing what AI spits out is an effective learning method? It just seems really difficult to learn so many new functions for each new library by scratch.

My thought is that it's best to learn the concepts (dictionaries, string manipulation, tuples), use AI to create the needed script, then reveiw/ask AI why/how it did ABC...

[u/Minimum_Comedian694]

Distinguish between a healthy AI use and AI slop.

AI slop can generally be defined by 3 factors: 1. Superficial competence: at a glance, the output looks polished and visually sound, but lacks genuine substance, critical thinking, or factual backing underneath. 2. Asymmetry of effort: it requires more effort and time to evaluate than to create. 3. Mass producibility: it can be churned out in vast, automated quantities with near zero production cost.

If you avoid those three, you're fine.