Why You Should Verify AI Refactors Against Real Output, Not Just Unit Tests

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AI-generated code refactors can introduce subtle bugs that pass unit tests yet break real-world behavior, as test mocks may never exercise the actual changed implementation. A common example is a config normalizer refactor that silently drops a required field like apiVersion, causing failures only when live API requests are made. Developers are advised to build a small executable harness that runs the real function against a known-good fixture and exits with an error if expected fields are missing. This harness should be run against both the original codebase and the AI-patched version using a Git worktree to isolate the comparison. The approach shifts validation from reading diffs carefully to producing concrete, reproducible evidence that the refactor preserves the original data contract.

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