Long output correlates with less laziness when writing code, and higher performance on benchmarks due to the monotone relationship between number of output tokens and scores. Comment spam correlates with better performance because it's locally-specific reasoning it can attend on when writing the next line of code, leading to reduced errors.
I have a prompt document that includes a complete summary of the Clean Code book, which includes the rules about comments.
You do have to remind it occasionally.
It has to reason about the problem in its output, since its output comprises almost the entirety of its "awareness". Unlike you, the LLM doesn't "know" anything, even superficial things.
In some sense it's like us when we are working on a problem with lots of novel parts. We usually have to write down notes to refer to in the process of solving the problem, except for the LLM the problem is always a novel problem.
I’ll experiment with comments, I can always delete them later. My strategy is to have self-documenting code (and my prompts include a how-to on self-documenting code)