Developer Uses Dual-AI Review System to Streamline Code Quality Decisions
A software developer has spent several months running every code change through a two-model AI review pipeline, where a second AI model audits work after the primary one completes it. Rather than catching bugs directly, the system's main impact has been forcing a structured triage process for handling review feedback. The developer settled on three response categories: act immediately on clear, low-risk comments; pause for human input on ambiguous or high-stakes suggestions; and silently ignore irrelevant or duplicate feedback. This framework addressed a common source of review fatigue — mishandling ambiguous comments by guessing at intent and triggering repeated re-reviews. Real-world examples from open-source pull requests illustrate how the three-bucket approach plays out in practice across different levels of complexity.
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