The rapid adoption of AI coding assistants has created an unexpected bottleneck: code review. As developers generate code faster with tools like Claude, the review queue grows, and reviewers face an overwhelming number of suggestions—many of which are noise. This article details the experience of the Molio team, which implemented an Open Code Review (OCR) process to filter AI-generated review comments. By applying structured criteria, they reduced irrelevant comments from 30 to just 12 per review, improving both speed and quality. The key insight is that AI review tools need human-guided filtering to be effective. For engineering leaders, this highlights the need to rethink review workflows as AI becomes more integrated into development. The OCR approach offers a practical template for teams facing similar challenges, balancing the speed of AI code generation with the rigor of human oversight.
As AI tools like Claude generate more code, teams find code review becoming a bottleneck. This post shares a real experience implementing Open Code Review (OCR) to filter AI review noise from 30 to 12 actionable comments. It highlights a growing challenge in AI-assisted development workflows.