That's the model I've arrived to as well:
- first I've created a skill how the architecture of the system should look like
- I'll tell the LLM to follow the guidelines; it will not do that 100%, but it will be good enough
- I'll go through what it produced, align to the template; if I like something (either I've not thought about the problem in that way, or simply forgot) I add that to the skill template
- rinse and repeat
This is not only for architecture of the system, but also when (and how to) write backend, frontend, e2e tests, docs. I know what I want to achieve = I know how the code should be organized and how it should work, I know how tests should be written. LLMs allow me to eliminate the tediousness of following the same template every time. Without these guardrails it switches patterns so often, creating unmaintainable crap
Bear in mind - the output requires constant supervision = LLM will touch something I told it not to touch, or not follow what I told it to do. The amount of the output can also sometimes be overwhelming (so, peer review is still needed), but at this point I can iterate over what LLM produces with it, with another LLM, then give to a human if it together makes sense