Interestingly AI has a "bathtub curve" for usefulness.
At the low level, Copilot is good at turning a few words of English into 2-4 lines of code. A super auto-complete. At the high level, Phind / ChatGPT can describe concepts and break them down one level. This works okay and is great to learn and iterate at the human level.
In the middle, AI doesn't work as well. Often when AI writes multipart code, each piece will be okay, but they don't connect together. I've seen instances where it obviously takes a function from project A, a function from project B, then handwaves a connection between them. If A and B have different assumptions, this doesn't work. For Dev code, this is annoying. For DevOps (resource code), this can be really bad. It's easier to validate Dev code.
I've used the Chain of Thought pattern with some luck. You give high-level requirements to the AI and ask it to write a series of steps, without code. Then you can iterate on the steps. Adding constraints, reordering the steps, etc. Lastly you say "okay give me code for step 3". This sort of works. If the AI forgets you can give it the steps again as a context.
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