PromptsApr 6, 2026·2 min read

AI Code Review Checklist — Ship Better with AI Help

Structured checklist for reviewing AI-generated code before merging. Covers correctness, security, performance, maintainability, and AI-specific pitfalls like hallucinated imports and phantom APIs.

PR
Prompt Lab · Community
Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

Before merging AI-generated code, run through this checklist:

Intro

AI coding agents write functional code fast, but they also introduce unique failure modes — hallucinated package imports, phantom API endpoints, outdated patterns, and over-engineered abstractions. This checklist is a structured review process specifically designed for AI-generated code, covering the standard quality checks plus AI-specific pitfalls that human-written code rarely has. Best for developers and team leads reviewing AI-generated PRs. Works with: any codebase, any AI agent.


AI Code Review Checklist

Correctness

  • Code compiles/runs without errors
  • All tests pass (existing + new)
  • Edge cases handled (null, empty, boundary values)
  • Error messages are helpful, not generic

AI-Specific Checks

  • No hallucinated imports (packages that do not exist)
  • No phantom API endpoints (URLs that were made up)
  • No outdated patterns (deprecated APIs, old syntax)
  • Function signatures match actual library versions
  • No circular dependencies introduced

Security

  • No hardcoded secrets or API keys
  • User input is validated and sanitized
  • SQL queries use parameterized statements
  • No eval() or dynamic code execution
  • Auth checks on protected routes

Performance

  • No N+1 query patterns
  • Large lists are paginated
  • Expensive operations are cached or lazy-loaded
  • No unnecessary re-renders (React)

Maintainability

  • Code follows project conventions (CLAUDE.md)
  • No over-engineering (YAGNI)
  • Variable names are descriptive
  • No dead code or commented-out blocks

---

## AI-Specific Pitfalls

### 1. Hallucinated Imports
AI invents packages that sound real but do not exist:

```python
# AI wrote this — package does not exist!
from fastapi_ratelimit import RateLimiter  # FAKE

# Verify: pip install fastapi_ratelimit → ERROR
# Real solution: use slowapi or custom middleware

Fix: Run pip install or npm install BEFORE merging. Check that all imports resolve.

2. Phantom API Endpoints

AI makes up API URLs based on naming conventions:

// AI assumed this endpoint exists — it does not!
const users = await fetch("/api/v2/users/bulk-update");

// Verify: Check your API docs or route files

Fix: Cross-reference every API call with your actual route definitions.

3. Outdated Patterns

AI uses deprecated APIs from its training data:

// AI used old React pattern
componentDidMount() { ... }  // Class component — should be useEffect

// AI used old Node.js
fs.readFile(path, 'utf8', callback)  // Callback — should be fs.promises

Fix: Check that generated code uses current APIs for your dependency versions.

4. Over-Engineering

AI loves abstractions, even when they are not needed:

// AI created a full factory pattern for a one-time operation
class UserServiceFactory {
  createService(type: string): IUserService { ... }
}

// You just needed:
async function getUser(id: string) { ... }

Fix: Ask "would I write this abstraction by hand?" If not, simplify.

5. Confident But Wrong

AI writes plausible-looking code that has subtle bugs:

# Looks correct, but off-by-one in pagination
items = db.query(Item).offset(page * page_size).limit(page_size)
# Should be: .offset((page - 1) * page_size)

Fix: Always verify business logic, especially math, dates, and pagination.

Review Process

Step 1: Compile & Run (30 seconds)

npm run build  # or equivalent
npm test

If it does not build, stop. Send back to AI.

Step 2: Dependency Check (1 minute)

# Check all imports actually exist
npm ls  # Node.js
pip check  # Python

Step 3: Quick Scan (2 minutes)

  • Read the diff, not the full files
  • Look for AI-specific pitfalls above
  • Check file names and structure match conventions

Step 4: Deep Review (5-10 minutes)

  • Verify business logic
  • Check security (OWASP basics)
  • Run through the full checklist above

FAQ

Q: Why do I need a special checklist for AI code? A: AI introduces unique failure modes (hallucinated imports, phantom APIs, outdated patterns) that traditional code review checklists do not cover.

Q: Should I review AI code differently than human code? A: Yes — trust but verify. AI code is more likely to be syntactically correct but semantically wrong. Focus on logic and dependencies, not formatting.

Q: How long should an AI code review take? A: 5-15 minutes for most changes. The checklist helps you focus on the highest-risk areas first.


🙏

Source & Thanks

Compiled from production experience reviewing thousands of AI-generated PRs. > > Share this checklist with your team to improve AI code quality.

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