The major tips I would add to this are: 0. ask it to ask you clarifying questions, which it rarely does by default when you're prompting it to do a task; often

The major tips I would add to this are: 0. ask it to ask you clarifying questions, which it rarely does by default when you're prompting it to do a task; often it has a few questions, which you can efficiently update your original prompt to cover. 1. make it generate a test-suite; have it iteratively generate new tests which don't overlap with the old ones. 2. ask GPT-4 explicitly to identify improvements, edge-cases and bugfixes, and then go through the list one by one having it rewrite for each one; not infrequently, a fancy rewrite will fail the test-suite from #1, but given the failing test-case, GPT-4 can fix it. 3. once it is clean and it's either done the list or you've disapproved the suggestions, and the test-suite is passing, ask it to write a summary design doc at the beginning. With all this, you're set up for fairly maintainable code: with the test-suite and the up-front design doc, future LLMs can handle it natively and well, and humans should be able to read it easily after #2 has finished, so you don't need to care where it came from or try to track 'taint' through all future refactorings or usage - GPT-4 can write pretty readable human-like code, it just doesn't necessarily do it the best way the first time (also like a human), so you have to apply inner-monologue ideas.