How to Evaluate AI-Generated Code Quality

AI can write code fast. But fast and correct aren't the same thing. Here's a practical framework for evaluating the code AI generates.

The Five-Point Review

Every piece of AI-generated code should pass these five checks before you ship it:

1. Does It Actually Work?

This sounds obvious, but AI code often works for the example case and breaks on real input. Test with:

• Empty inputs — what happens when fields are blank?
• Boundary values — zero, negative numbers, very long strings
• Invalid data — wrong types, malformed JSON, missing fields
• Concurrent access — two users hitting the same endpoint

2. Is It Secure?

AI models don't think about attackers. Check for:

• SQL injection — is user input parameterized or concatenated into queries?
• XSS — is user content escaped before rendering?
• Auth/authz gaps — can users access resources they shouldn't?
• Exposed secrets — are API keys hardcoded? Are error messages too verbose?
• Rate limiting — can someone abuse the endpoint with rapid requests?

3. Is It Readable?

Code is read far more than it's written. Look for:

• Clear variable names — userData beats d, isAuthenticated beats flag
• Consistent style — does it match the rest of your codebase?
• Appropriate comments — explaining why, not what
• Reasonable function size — if a function is 100+ lines, it probably needs splitting

4. Is It Efficient?

AI often generates the correct but naive solution:

• N+1 queries — fetching related data in a loop instead of a join
• Unnecessary re-renders — missing useMemo, useCallback, or proper keys
• Blocking operations — synchronous file reads in request handlers
• Memory leaks — event listeners without cleanup, growing arrays without limits

5. Is It Maintainable?

Think about the developer who reads this code in six months:

• No magic numbers — use named constants
• Clear error handling — specific catch blocks, helpful messages
• Proper types — TypeScript interfaces, not any
• Reasonable dependencies — does this need a library, or is 10 lines of code enough?

Red Flags in AI-Generated Code

Watch for these patterns that suggest the AI didn't understand the requirements:

• Overly generic variable names like data, result, temp, obj
• Comments that restate the code — // increment counter above counter++
• Unused imports — AI often adds imports it doesn't use
• Hardcoded values that should be configurable
• Copy-paste patterns that should be abstracted into functions
• Missing error handling in async operations

Building a Review Habit

The goal isn't to review every line with suspicion. It's to build efficient patterns:

1. Skim the structure first — does the overall approach make sense?
2. Check the boundaries — inputs, outputs, error paths
3. Read the logic — is the core algorithm correct?
4. Test the edge cases — what didn't the AI think of?

Over time, you'll develop an intuition for which AI outputs need heavy review and which are reliable. That intuition is what separates productive vibecoding from reckless vibecoding.

Use Tools to Help

You don't have to do this alone:

• Our Vibe Checker reviews code for bugs, security, and improvements
• Linters (ESLint, Prettier) catch style and basic quality issues automatically
• Type checkers (TypeScript strict mode) catch type errors before runtime
• Our Accessibility Checker audits UI code for WCAG compliance

The best workflow: generate with AI, review with tools, verify with your brain.