The Human Layer: Code Review for Generated Artifacts

A pragmatic checklist for reviewing AI-written code.

Reviewers should focus on invariants, error handling, and boundary conditions, not superficial style. Confirm inputs are validated, side effects are explicit, and resources are cleaned. Run perf smoke tests and scan for dependency risk and license contamination.
A short checklist beats vague “LGTM”: data flows, authz, idempotency, and observability hooks. With a crisp rubric, AI accelerates work while humans guarantee integrity.

code-review

checklists

quality

tooling

Gallery

Back to Knowledge Center

Have a project in mind?

We'd love to hear about what you're building.