From AI-Generated Code to Production: A Developer's Checklist

1. Run Automated Code Quality Analysis

Before involving human reviewers, it is critical to eliminate the most obvious and repeatable problems automatically. Automated analysis is the fastest way to surface high-risk issues across an entire change set.

Focus on identifying:

• Injection risks, unsafe query construction, and unvalidated inputs
• Missing authorization checks on sensitive routes
• Expensive loops, repeated queries, and unbounded operations
• Duplicate code and high-complexity hotspots
• Type-safety regressions

A simple rule helps here: if an automated scan flags something as high severity, fix it before asking a teammate to review the pull request.

2. Confirm Authentication and Authorization

Many AI-generated issues are not logic errors but permission errors. Code works correctly, just for the wrong audience. Before merging, validate that access controls are intentional and enforced at the correct boundaries.

Questions to ask:

• Should this endpoint or action require authentication?
• If authenticated, should every user be allowed, or only certain roles?
• Can a user access or modify another user's data?
• Are administrative actions properly gated?
• Is there audit logging for destructive actions?

A simple test often reveals problems: try the action as a non-privileged user in staging. If it succeeds, authorization is likely missing.

3. Validate and Sanitize Input

AI models frequently assume inputs are well-formed. Production traffic never is. Input validation is your first line of defense against crashes, data corruption, and exploits.

Verify that:

• Types, formats, and lengths are validated
• Unexpected fields are rejected
• File uploads are constrained by type and size
• Inputs are sanitized before database usage or HTML rendering

Simple adversarial tests, such as sending empty values, invalid formats, or extra fields, often expose missing validation immediately.

4. Check Database and Query Behavior Under Scale

Some of the most costly AI-generated bugs are invisible in development environments. They only appear when real data volumes hit the system. Before merging, review database access patterns carefully.

Look for:

• Queries inside loops
• Overfetching such as SELECT star
• Missing pagination on list endpoints
• Missing or unused indexes
• Large blobs loaded unnecessarily

Run the endpoint against a large dataset and observe query count and execution time. If response time grows disproportionately as data grows, you have a scaling issue.

5. Inspect Algorithmic Complexity

Correct code can still be dangerously slow. AI-generated logic often prioritizes clarity over efficiency, even when performance matters.

Review for:

• Nested loops over large arrays
• Repeated linear searches that should use maps or sets
• Heavy computation happening synchronously on request paths

A quick benchmark using larger inputs often reveals problems early. If runtime explodes with input size, optimize before shipping.

6. Add Production-Grade Error Handling

AI-generated code often omits the failure paths that matter most in production. Before merging, confirm that failures are anticipated and handled gracefully.

Ensure that:

• External calls have timeouts
• Retries are safe and bounded
• Errors are logged with enough context to debug
• Users see meaningful feedback rather than crashes
• Failures degrade functionality rather than bringing systems down

Unhandled promise rejections and silent failures are strong signals that error handling is incomplete.

7. Enforce Your Project Standards

Consistency is a form of risk reduction. Code that violates established conventions is harder to reason about and easier to break.

Check that:

• The change follows existing layering and patterns
• Naming is consistent and intentional
• Type safety is not weakened
• New helpers live in appropriate modules

For TypeScript projects, avoid introducing new any types without strong justification and maintain strict compiler settings wherever possible.

8. Tests with Minimum Viable Coverage

AI can generate tests, but it rarely generates the right tests. Human judgment is still required.

At a minimum, ensure coverage for:

• The happy path
• Invalid input
• Not found or empty results
• Permission denied scenarios
• External dependency failures
• Edge cases involving missing or null values

If writing tests feels unusually difficult, that is often a signal that the design is too tightly coupled.

9. Human Review Focused on Intent and Trade-Offs

Automated checks find patterns. Humans evaluate intent.

During review, focus on:

• Whether the solution addresses the real problem
• Whether the approach is appropriately simple
• Alignment with system architecture
• Long-term maintainability
• Product and user experience implications

Including this checklist in pull request descriptions helps keep reviews focused and efficient.

10. Post-Deploy Monitoring as a Safety Net

Even strong pre-production checks cannot predict every real-world failure.

After deployment, actively monitor:

• New error types
• Latency increases
• Resource usage spikes
• Suspicious authentication behavior
• Signals that warrant rollback

If you cannot observe it, you cannot safely ship it.