People are thinking too much of humans and LOCs as something valuable or worth their consideration when working with AI (because usually that LOCs would have required human effort). This is simply not the case when doing AI coding, and you need to adjust how you work because of that and play to the strengths of this setup, if you want to get something out of it and not frustrate yourself.
Here is how to do this: Have it generate something. That first 2000 lines of not so great first attempt code, don't even think about understanding all of that, or, worse, about correcting it.
Review it loosely. You are not dealing with a human! There is absolutely no need to be thorough or nice. You are not hurting any feelings. Go for 80/20 (or the best ratio you think you can get).
Then, think:
- Anything you missed to inform the AI about? Update your initial prompt
- Anything the AI simply does not do well or to your liking? Write general instructions (all of the IDEs have some way of doing that) that are very explicit about what you don't want to see again, and what you want to see instead.
Then revert everything the ai did, and have it go again from the start. You should approach something that's better.
This approach is essentially the PR workflow preferred by the author. Why let an LLM make huge changes to your working copy just for you to revert them next, instead of just writing patches to be asynchronously reviewed? What you propose is no way of doing pair programming in particular, and seems to support the author’s argument.
1. There is not a mention of "pair programming" in the comment I was addressing. As often happens, the discussion evolves.
2. The point is, that you are training the AI through this process. You can do pair programming afterwards (or not). Aim to instruct it to give you ballpark answers first, and take it from there.
> LOCs as something valuable or worth their consideration when working with AI (because usually that LOCs would have required human effort)
At this point AI generated code absolutely requires review by a human so LOC is still an important metric.