Feedback Flywheel

Teams using AI coding assistants often plateau because individual learnings never become shared practice. A structured feedback flywheel addresses this by categorizing AI interaction signals into four types — context, instruction, workflow, and failure — and routing each back into specific shared artifacts like priming documents, shared commands, and team playbooks. The practice operates at four cadences: after each session, at standups, at retrospectives, and periodically. Measuring effectiveness focuses on first-pass acceptance rate, iteration cycles, and principle alignment rather than raw speed. The key insight is treating AI infrastructure artifacts as living documents requiring the same maintenance discipline as test suites, not one-time setup documentation.