Good luck with that. I used to be an OCD freak about code before LLMs, but AI coding has largely freed me of that limitation. I've become very comfortable giving AI a long leash, but only after being meticulous about curating the context.

These days I spend most of the day in discussions and planning, producing documentation, agonizing over architectural decisions, edge cases, and naming conventions. Once that's all settled I'll hand off implementation work to run overnight. In the morning, I'll review and fix, but I'm usually pleasantly surprised with the results.

One pitfall is long leash without a curated context, which is more like "slot machine" coding. Usually not effective, and may have addictive effects since it does occasionally work.

To spice things up lately, I've been encouraging the model to produce its own "capstone" -- a feature it decides to build on its own, however it wishes, with the tools at its disposal. So far it's been conservative, creating useful tools for development rather than customer facing features, but I'm curious to dial up the temperature to see what it might come up with.