Creator here. This article came from a pattern I observed across dozens of AI-augmented development teams: initial velocity skyrockets with tools like Cursor AI and GitHub Copilot, but productivity crashes months later when teams need to modify that same codebase.

The root issue is consistent: AI accelerates code generation while simultaneously eliminating the contextual knowledge that traditionally accompanies development. The decision-making that normally happens during programming gets compressed into ephemeral chat sessions that disappear instantly.

Our solution is based on MDC (Machine-Digestible Context) rules - a framework for systematically capturing context during AI generation without slowing down the development process. The key insight was that we needed to make documentation part of the workflow rather than a separate task.

Teams implementing this approach report 40% faster onboarding for new developers and a 67% reduction in context-related questions.

I'd love feedback from those who've experienced similar context issues with AI-assisted coding or thoughts on the sustainability of vibe coding approaches in professional environments.