Have you built anything purely with LLM which is novel and is used by people who expect that their data is managed securely, and the application is well maintained so they can trust it?
I have been writing specifications, rfcs, adrs, conducting architecture reviews, code reviews and what not for quite a bit of time now. Also I’ve driven cross organisational product initiatives etc. I’m experimenting with openspec with my team now on a brownfield project and have some good results.
Having said all that I seriously doubt that if you treat the english language spec and your pm oversight as the sole QA pillars of a stochastic model transformer you are making a mistake.
The issue is that validation needs presence and it is the limiting factor - common knowledge, but is part of the “physics”. Also maintenance gets really tricky if the codebase has warts in it - which it will have. I get much more easy to understand architecture out of an LLM driven code generation process if I follow it and course correct / update the spec process based on learnings.
Example: yesterday I’ve introduced a batch job and realized during the implementation phase that some refactoring is needed so the error boundary can be reused in the batch application from the main backend. This was unplanned and definitely not a functional requirement - could be documented as non-functional. There was a gap between the agent’s knowledge and mine even though the error handling pattern is well documented in the repository. Of course this can be documented better next time if we update the process of openspec writing but having these gaps is inevitable unless formal and half-formal definitions are introduced - but still there needs to be someone with “fresh eyes” in the loop.
