AI coding tools can generate patches quickly, but maintainers still cannot merge a PR on generated code alone. They need evidence, what was reproduced, what changed, how it was validated, and whether the contributor actually reduced review work instead of shifting it downstream.

In this session, I draw on recurring patterns from reviewing and maintaining Argo CD contributions to show why many AI-assisted PRs stall and what makes others move quickly. I present a simple four-stage framework for review-ready OSS contributions, Set up the environment, validate the problem before changing code, harden the fix with tests and checks, and submit a PR with enough context and proof for efficient review. Attendees will leave with practical guidance they can use immediately to improve contribution quality and reduce maintainer burden.