[chunky1994]

This is exactly why using GPT to supplement but not supplant writing code is very good. Code generation is generally more helpful as you go to higher layers of abstraction as long as you can stay at that layer of abstraction. Once you need to jump a level lower because your abstraction fails in a specific context you need pre-existing knowledge of how the abstraction works in order to ask GPT the right questions.

Hence, depending on the domain knowledge of the piece of code you are dealing with, GPT can be very helpful in generating a good scaffolding to get off the ground quickly (i.e you want to write a web app but don't want to deal with having to learn how to write a whole react app etc.), but asking GPT to...write an optimizer for a C runtime would end up with poor results as its heavily bent on the specifics of that task where a specialists' knowledge would outweigh any abstractive advantages.

One very useful experiment I did early on was try to solve a problem with GPT where I had deep domain expertise and see where the cracks are vs in one in which I had very poor expertise. This led me to make my abstraction based statement above, and so far I've seen it remain true with every successive version.