AI-generated code needs the same standards as human code, with extra attention to intent, invented APIs, shallow tests, dependency choices, and maintainability across the full change.
AI-generated code does not require a separate definition of correctness. It must satisfy the same product behavior, security policy, tests, and maintenance standards as any other contribution. What changes is the failure distribution. A generator can produce a large, polished patch quickly, using plausible APIs and comprehensive-looking tests. The surface coherence can make reviewers assume more intent and verification than the authoring process actually contained.
The first review question is not whether the code looks idiomatic. It is whether the pull request states a falsifiable goal. Ask for the behavior being changed, constraints that must remain true, and evidence that the change was exercised. If the author cannot explain why each part exists, reduce the patch before reviewing it. Provenance is less important than ownership: a human must be able to defend and maintain the submitted result.
Generated code often compiles against an assumed contract that is subtly wrong: an SDK option from another version, an error type that is never thrown, a framework lifecycle that does not apply, or a database method with different transaction behavior. Verify imported APIs against the installed version and local usage. Trace inputs, outputs, nullability, errors, and state changes across the changed boundary. A clean helper cannot rescue an incorrect contract.
A generated test suite may mirror implementation branches without proving user-visible behavior. Look for assertions that restate mocked return values, mocks that bypass the code under test, snapshots that accept an entire new structure without scrutiny, and missing negative cases. Mutation thinking helps: name one realistic defect in the implementation and ask which test catches it. If no test would, add one before trusting the green check.
Inspect dependency and configuration changes carefully. Generators can add a package to solve a small problem already handled by the platform, choose an abandoned-looking interface from training data, or create overlapping configuration. Confirm ownership, license policy, runtime compatibility, bundle impact where relevant, and whether an existing dependency already provides the capability. Generated lockfile churn should not hide the intentional change.
An AI reviewer can be helpful because it approaches the patch with a fresh prompt and can trace failure modes the generator did not consider. It can also share the same blind spots, especially when both rely on plausible framework knowledge. Require evidence tied to repository context and keep comments advisory. Deterministic type checks, tests, linters, security tools, and human review remain essential.
Generation speed is not a reason to accept oversized pull requests. Separate mechanical changes from behavioral ones, avoid unrelated cleanup, and explain generated sections that are intentionally repetitive. Small commits can reveal the reasoning sequence even if code was generated in larger bursts. The scalable policy is straightforward: authors may use any tool, but they submit only code they understand, evidence they have checked, and a diff sized for another person to verify.
Teams can encode that policy in the pull-request template without demanding disclosure theater. Ask for intent, verification commands, risky boundaries, generated artifacts, and manual checks. These questions improve every contribution regardless of authorship. If provenance must be recorded for legal or governance reasons, handle it explicitly and separately from technical review so reviewers do not mistake tool disclosure for proof that the patch is safe.
PR Quorum turns specialist reviewer output into one clean GitHub review, with noise controls and predictable usage caps.