Yeah, tried similar things.

The issue is that having them learn that on it's own is currently an inaccurate process with a lot of overlooking. I recently tried doing some of the techniques that fared well on smaller repositories on a giant monorepo, and while sometimes they did yield improvements, most often things got overlooked, dependencies forgot about, testing suites confused. And it wastes a ton of compute in the end for smaller yields.

It will get better, that I am sure of, but currently the best way is to introduce it an architecture, give it some samples so it can do what it does best - follow text patterns. But people are mostly trying to one-shot things with this magical AI they heard about without any proper investment of time and mindshare into it.

While some might say "oh that wont work well in legacy repositores, we got 6 architectures here", pointing that out and adding a markdown explaining each helps a ton. And not "hey claude generate me an architecture.md" but transferring the actual knowledge you have, together with all the thorny bits into documentation, which will both improve your AI usage and your organisation.