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LLMs can be very useful if you understand how they work. The danger is when you assume that its correct.
This. Sorry but I'm a web developer and one of my colleagues obviously uses it without checking if it is correct, then bugs me or others when he doesn't understand why it doesn't work as expected. It is frustrating as hell and I've explained it to him multiple times:
Over prompt the AI if you are going to use it. Long lengthy prompts that are very succinct but give as much context as possible.
It is highly preferable to check other sources first like Stack Overflow. Even Medium articles can be better than using AI sometimes.
Type out what the AI output rather than just copy and paste. As you type line by line, explain to yourself what is happening.
Question everything. Do you think this code will work. Why will it work?
Test the code. If it doesn't work as expected, trouble shoot it.
Don't be afraid to scrap the whole thing and start over. Even open another prompt and try again if you really think the AI can answer the question (there are many cases where your problem is just too specific and the AI can't).
He does none of these things. I swear he is the laziest developer I've ever met, and I've met my fair share.
That sounds like more work than just doing the thing.
I have copilot in my IDE and it's basically worse than the 'dumb' autocomplete that just builds off the existing codebase. Copilot knows all the code on GitHub, but doesn't seem to know anything about the local codebase.
As I suspected, I'm better off just doing the fucking thing myself.
Don't blame all of AI because your coworker is lazy.
Also its best not to create really long prompts as that can confuse it. Instead, do your job. Its good for smaller things but it can't replace a human.
I've found that just asking "did you make that up again?" after every response improves the quality of code Chat GPT produces. It seems to pick up fairly quickly on methods it just invented.