this is the right way to try and tackle this problem imo. too much focus in AI dev tooling has been on building "products" that only half work.

making codebases understandable to humans, and LLMs etc, is a better approach

self documenting, interpretable systems would actually solve a lot of dev churn in big companies

plus it's not like artifacts have to be limited to code once that's figured out

I don't think it's a choice; I use both. Code understanding is especially useful in new code bases, but once that's over you need to get work done.