I don’t know, I could be wrong but I think one of the key aspects of system design which I don’t know if there is a lot of training data for us the “why” behind decisions. Separating out good design from bad isn’t always black and white and like the article mentions it’s about managing complexity and trade offs. It’s hard to capture in code/training data “we designed everything in a certain way but compromised in this one area because we were under time constraints and assumed we could fix it later”

A fun little exercise you can do is design a system and write some code and then ask LLM to explain why you wrote it that way. Results are varied and interesting but in my experience rarely capture the actual why behind decisions.

Interestingly, while it may not match the reasoning that I have, I find that it usually comes up with things that I should have thought of.