How One Team Reviewed AI-Written Code Without Letting Quality Slip

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A software team that let AI draft roughly a third of its codebase found that faster code generation did not reduce the cost of owning, testing, or maintaining that code. To manage the increased volume, they introduced a mandatory automated gate — covering type checks, linting, static analysis, secret scanning, and test coverage — before any AI-generated change reached a human reviewer. The team also established a firm cultural rule: whoever opens the pull request is fully accountable for every line, regardless of whether a model wrote it. They identified common failure patterns in generated code, such as edge-case mishandling and hallucinated API calls, and built a focused reviewer checklist around these. Rather than tracking lines generated, the team monitored change-failure rate and defect-escape rate, using upticks in those metrics as signals that review discipline needed tightening.

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