AI as Your Tireless Senior Reviewer
Code review is the highest-leverage activity in software development. It catches bugs before they reach production, enforces standards, and spreads knowledge across a team. But human reviewers are expensive, slow, and unavailable at 2am when you are in flow.
AI code review is not a replacement for human judgment on critical systems. But for the solo developer or small team, it is the closest thing to having a senior engineer on call 24/7.
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The Four Review Lenses
Structure your AI code reviews around four distinct perspectives. Each catches different categories of problems.
Lens 1: Correctness (Bug Hunt)
code
Review this code for correctness bugs only. Look for:
- Off-by-one errors in loops and array access
- Unhandled async/await errors (missing try/catch)
- Race conditions in concurrent operations
- Null/undefined dereferences that could crash at runtime
- Logic errors where the code does the wrong thing in edge cases
- Incorrect assumptions about data types or shapes
For each bug found: show the exact line, explain what goes wrong, and write the corrected version.
[paste your code]
Lens 2: Security
code
Perform a security audit of this code. Check for:
- SQL/NoSQL injection vulnerabilities
- Missing authentication/authorisation checks on sensitive operations
- User-controlled data being used in dangerous operations (eval, fs operations, shell commands)
- Sensitive data (passwords, API keys, PII) being logged or returned to the client
- Missing rate limiting on expensive operations
- CORS misconfiguration
For each issue: classify as Critical/High/Medium/Low, explain the attack vector, and provide a fix.
[paste your code]
code
Analyse this code for performance problems. Look for:
- N+1 database query patterns (querying inside loops)
- Missing database indexes for queried fields
- Expensive operations being repeated when they could be cached or memoised
- Large data being fetched when only a subset is needed (SELECT * problems)
- Synchronous operations blocking the event loop
- Missing pagination on queries that could return large datasets
For each issue: estimate the impact (low traffic vs. high traffic), explain why it matters, and suggest the fix.
[paste your code]
Lens 4: Maintainability
code
Review this code for long-term maintainability. Identify:
- Functions longer than 50 lines that should be split
- Duplicated logic that should be extracted into shared utilities
- Magic numbers and strings that should be named constants
- Missing or misleading comments on non-obvious logic
- Tightly coupled modules that are hard to test in isolation
- Inconsistent naming conventions
For each issue: explain the cost of leaving it as-is, and suggest the refactor.
[paste your code]
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The Full-File Refactor Prompt
Once you have identified issues, use this prompt to get a clean, refactored version:
code
Here is the original file and a list of identified issues. Refactor the file to fix all issues while:
- Keeping the same external API (function signatures, exports)
- Maintaining backward compatibility
- Adding comments explaining the key changes made
Original file:
[paste file]
Issues to fix:
[paste issues from review]
Output the complete refactored file.
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Building a Review Into Your Workflow
The best time to run an AI code review is:
- Before committing — paste the diff into Claude or GPT and run the correctness lens
- Before deploying — run the security lens on your API routes
- Weekly refactor session — pick 2-3 files, run the maintainability lens, make improvements
Make it a habit. Code quality compounds.