Comment by joshvm

I agree on the distillation step. They're very good at identifying what problem you're trying to solve, especially if you don't have much experience in the domain. I've found ChatGPT great for figuring out problems involving combinatorics. And then you can be canny and ask the LLM to write the next prompt to get some boilerplate code. I like to do this with ChatGPT 4.5 (heavily rate limited) targeting 4o.

Getting good code is still a pain with chat, but you can go a fair way by reducing the context as much as possible and only asking for core functions (no point wasting hundreds of tokens on an arg parser). I'd like to play with Cursor or Zed a bit more.

Debugging sometimes works. 4.5 often figures out errors in one, 4o often struggles and spits out suggestions like it's a customer support agent (it also falls into repetition fairly often if you tell it "no, so what else could it be?"). You really have to work to convince it up front that you're not an idiot.