Scope
Confirm the assistant changed the intended behavior and preserved non-goals.
AI code review preview
Review AI-generated code as a proposal, not as finished work.
Use this checklist when Claude, Codex, GitHub Copilot, Cursor, Kiro, Windsurf, or another assistant creates code, tests, configuration, documentation, or pull request changes.
Review areas
What every AI-generated code review should cover
The implementation-ready checklist is included in the AIDLC Team Starter Kit.
Correctness
Review logic, edge cases, dependencies, data handling, and maintainability.
Evidence
Record checks, product acceptance, release notes, and open decisions by risk route.
Need the full review checklist?
The complete pull-request-ready checklist is part of the AIDLC Team Starter Kit.
Review gates
Use the checklist differently by risk
Low risk
Internal, reversible, or documentation-heavy changes.
- Scope check.
- Basic tests.
- Reviewer explanation.
Medium risk
Customer-visible, workflow, dependency, or release-impacting changes.
- Regression tests.
- Product acceptance.
- Evidence ledger row.
High risk
Security, privacy, infrastructure, irreversible, compliance, or production-sensitive changes.
- Human approval before execution.
- Rollback plan.
- Security and operations review.
FAQ
AI code review questions
How should teams review AI-generated code?
Review it like a proposed patch from a fast but fallible contributor. Check scope, correctness, tests, security, dependencies, maintainability, product acceptance, and evidence.
What is the biggest AI code review mistake?
The biggest mistake is reviewing only whether the code compiles. Teams must also review whether the assistant changed the right thing, avoided hidden scope expansion, and left enough evidence for future maintainers.
Should AI-generated code require stronger review?
Yes when the change affects production behavior, infrastructure, permissions, privacy, security, or customer workflows. The risk route should decide the review depth.
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