Over the past few months I built an AI-assisted delivery framework — not to write code faster, but to eliminate ambiguity across the entire software development lifecycle.
The result completely changed how I think about AI in engineering.
The problem I kept hitting
Every time I used AI to generate architecture docs, API contracts, or implementation plans across separate sessions, the outputs looked great in isolation. But viewed together? They were broken. A pivot in the system architecture was never reflected in the API contracts. Frontend assumptions silently diverged from backend data models.
AI wasn't the problem. Treating it as a collection of disconnected prompt sessions was.
