As long as you're reinventing the wheel (implementing some common pattern because you don't want to pull in an entire dependency), that kind of AI generation works quite well. Especially if you also have the AI generate tests for its code, so you can force it to iterate on itself while it gets things wrong the first couple of tries. It's slow and resource intensive, but it'll generate something mostly complete most of the time.

I'm not sure if you're saving any time there, though. Perhaps if you give an LLM task before ending the work day so it can churn away for a while unattended, it may generate a decent implementation. There's a good chance you need to throw out the work too; you can't rely on it, but it can be a nice bonus if you're lucky.

I've found that this only works on expensive models with large context windows and limited API calls, though. The amount of energy wasted on shit code that gets reverted must be tremendous.

I hope the AI industry makes true on its promise that it'll solve the whole inefficiency problem because the way things are going now, the industry isn't sustainable.

Yeah it can, though rough is definitely the word.

And sometimes the LLM just won't go in the direction you want, but that's OK - you just have to go write those bits of code.

It can be suprising where it works and where it doesn't.

Just go with those first suggestions though and the code will end up rough.

The leading models have been very good at this for over a year now. Try copying one your existing C++ header files into GPT-5 or Claude 4 or Gemini 2.5 as an experiment and see how they do.

They certainly invent new functions whenever I try.

You can do this already, the most useful things to help with this are either writing tests or having it write tests and telling it how to compile and see error messages so you can let it loop.