How to Review Cursor-Generated Code Before Merging (2026 Guide)
Cursor can generate production-ready features in minutes. But AI-generated code requires a different review process than human-written code.
Fluent code is not the same as correct code. Before merging a Cursor-generated PR, you need a structured review checklist that catches hallucinations, missing auth logic, cloud cost risks, and silent edge-case failures.
Why AI-Generated Code Needs a Different Review Process
Traditional code review assumes:
- A developer understood the full system context
- Edge cases were consciously considered
- Dependencies were intentionally chosen
With Cursor, the AI may:
- Infer missing interfaces
- Hallucinate helper utilities
- Over-permission IAM roles
- Skip authorization checks
- Add cost-heavy cloud configurations
Your review must adapt to these patterns.
Step 1: Verify Authentication and Authorization
AI frequently implements functionality but forgets enforcement.
Checklist:
- Are routes protected by auth middleware?
- Are role checks explicit?
- Are admin-only paths properly scoped?
- Are sensitive endpoints rate-limited?
Missing auth is one of the most common AI-generated risks.
Step 2: Detect Hallucinated Dependencies
Cursor may reference utilities, packages, or functions that do not exist.
Review for:
- Imports that aren’t in package.json
- Helper functions that don’t exist in the repo
- Inconsistent naming conventions
- Assumed database fields
If something “looks plausible” but you don’t remember creating it — verify it exists.
Step 3: Check for Silent Cloud Cost Risks
AI optimizes for functionality, not cost.
Look for:
- Unbounded queries
- Missing pagination
- Large data fetches without limits
- Expensive cloud service defaults
A small AI-generated change can increase cloud cost significantly.
Step 4: Validate Edge Cases Explicitly
AI-generated code often handles happy paths well but misses:
- Null inputs
- Empty states
- Timeout scenarios
- Concurrency conflicts
- Permission edge cases
Reviewers should simulate failure states mentally before approving.
Step 5: Ensure Tests Cover AI Changes
Ask:
What new tests should exist for this PR?
If Cursor generated new logic, tests should reflect:
- Error handling
- Authorization logic
- Boundary conditions
- Performance constraints
Automating AI Code Review in GitHub
Manual review works — but it doesn’t scale.
Tools like Codebase X-Ray scan PR diffs specifically for AI-introduced risks and generate a fix branch automatically.
Instead of:
- Manually checking auth every time
- Scanning for hallucinated imports
- Estimating cloud cost impact
You can run an automated AI code review before merging.
Try 3 free PR scans at prodmoh.com.
Conclusion
Cursor dramatically increases development speed. But speed without structured review increases production risk.
By adapting your code review process for AI-generated PRs — and automating verification where possible — you can ship faster without merging blind.