I use LLM's for my developments constantly, so I have a pretty good grasp of what works and doesn't work. Github copilot really gets it, as in: it's a "co-pilo

I use LLM's for my developments constantly, so I have a pretty good grasp of what works and doesn't work. Github copilot really gets it, as in: it's a "co-pilot", and you are the "pilot". LLM's are able to generate code way faster than me, so in a lot of cases, writing a prompt, letting the LLM's generate a diff and me quickly reviewing, is faster than me writing/shuffling all the code. This is basically a "I know what has to be done, I just have to do it". But let's be clear: LLM's are useful in small steps, not huge steps. Huge steps are only possible if you have a blank page. I have code that was written by an LLM and I never really reviewed it: It's a visual effect in shaders that works, I quickly glanced over the code and it seemed complicated and fine. Since this is not crucial code, it works, and I don't have to touch it, it's fine for me. Also an observation: once the LLM gets it wrong, it will continuously get it wrong after different instructions. After 1 or 2 failures, quickly decide to write it yourself. The least amount of benefit any developer should get out of it is an "apply stack overflow suggestion". In the "old" days you searched google for your issue, read Stack Overflow comments, and try to apply it. LLM's let you shortcut this by going straight from prompt to diff suggestion. You can push it a bit further than a "Stack Overflow on steroids", but don't expect it to maintain a codebase by itself.