PR Review Checklist for AI-Generated Code

Pull requests created with AI coding assistants (like Cursor, GitHub Copilot, or Claude) look remarkably clean at first glance. The formatting is tidy, the comments are polite, and the automated summary sounds completely confident.

That surface polish is precisely why AI-generated code is dangerous when reviewed casually. Unlike human juniors who leave telltale syntax errors or messy indentation when they are confused, language models generate syntactically perfect code that can be subtly wrong in architecture, security, or error handling.

Here is the practical review checklist I use on real production codebases to catch silent regressions, maintain code standards, and ensure AI code remains an asset rather than technical debt.

Why AI Pull Requests Need a Different Review Posture

Human engineers usually make mistakes because they misunderstand syntax or miss a library feature. AI models make mistakes because they lack genuine architectural intent:

• They solve local problems by duplicating global code: If the model cannot easily find an existing date formatting helper or API client, it silently writes a new one in the current folder.
• They hallucinate non-existent parameters: The model may invoke a third-party library method with options that look plausible but are unsupported in your installed version.
• They gloss over failure paths: Happy paths are generated thoroughly, while network timeouts, database reconnections, and null checks are frequently skipped.

The 5-Point Review Checklist

1. Scope and Blast Radius

• Did the PR touch files outside the agreed feature boundary?
• Were existing configuration files (such as package.json, tsconfig.json, or lint configs) modified unnecessarily?
• Did any new packages or dependencies get added to the lockfile without team approval?

2. Architecture and Code Reuse

• Does this PR duplicate logic, constants, or types that already exist elsewhere in the repository?
• Does the code follow your project’s established conventions (e.g., Server Components vs Client Components in Next.js, or repository patterns in backend services)?
• Are state boundaries and component responsibilities properly separated?

3. Security and Data Validation

• Are all incoming user inputs, route parameters, and request payloads strictly validated with a schema (such as Zod)?
• Are authorization checks enforced on server actions and API routes, rather than relying solely on client-side UI visibility?
• Are sensitive tokens, API secrets, or internal error messages exposed to client bundles or browser logs?

4. Error Handling and Edge Cases

• What happens when downstream APIs return a 4xx, 5xx, or network timeout? Are errors surfaced gracefully to the user?
• Are loading, empty, and partial states properly rendered in the UI?
• Were try/catch blocks added with proper logging, or are errors silently swallowed?

5. Automated Tests and Verification

• Are unit and integration tests included for the new logic, testing both success and failure conditions?
• Do the tests actually assert business requirements, or did the AI write trivial tests that pass regardless of logic bugs?
• Does tsc --noEmit and the project linter pass cleanly without warnings?

Copy-Pasteable PR Review Template for Teams

You can drop this markdown template directly into your team’s .github/pull_request_template.md to standardize reviews across your engineering team:

### AI-Assisted Code Verification Checklist

- [ ] **Boundary Check:** All modified files are strictly within the intended feature scope.
- [ ] **No Unvetted Deps:** No unapproved packages added to package.json / lockfile.
- [ ] **Deduplication:** Verified that existing helpers and types were reused rather than recreated.
- [ ] **Security & Auth:** Server-side input validation and auth checks are explicitly tested.
- [ ] **Error Handling:** Empty, loading, and failure states are tested and accounted for.
- [ ] **Real Tests:** Unit/integration tests verify business outcomes, not just mock tautologies.
- [ ] **Clean Lint & Types:** Typecheck and linters pass with zero warnings.

Building Better Review Habits Across Your Team

AI tools can double an engineering team’s output, but only if your code review process adapts to catch architectural and subtle logic errors before they reach production.

If you want to train your engineering team on setting up Cursor project rules, review workflows, and PR standards, explore my Cursor mentoring and team coaching services.