How to Review AI-Generated Code Before Shipping: A Developer Checklist

AI coding tools generate code faster than most teams can review it. That is the point. But fast generation without systematic review is how critical bugs reach production.

This is not a theoretical risk. SQL injection, authentication bypasses, and silent API failures are consistently the most common findings in AI-generated codebases. They are also consistently preventable with a structured review before deployment.

Here is the checklist I run on every AI-generated file before it ships.

Why does AI-generated code need a different review approach?

Traditional code review focuses on architecture decisions, naming conventions, and logic correctness. A senior engineer reads the code and applies their pattern recognition.

AI-generated code has a specific failure profile that pattern recognition misses: the surrounding code is often excellent, which creates a false sense of security. The vulnerability is isolated, subtle, and in exactly the place where human reviewers stop paying attention.

Automated specialist review catches these reliably because it scans every line with equal attention, regardless of how clean the surrounding context looks.

The five-point pre-deployment checklist

1. Authentication and authorisation boundaries

Every endpoint that returns user data or performs user actions needs a server-side auth check. AI tools frequently implement client-side validation that can be bypassed by modifying the request.

Check: is the session token validated on the server before any data is returned? Can a user access another user's resources by changing an ID in the request?

// Common AI pattern - dangerous
if (req.query.userId === currentUser.id) {
  return getUserData(req.query.userId)
}

// Correct pattern
const user = await validateSession(req.headers.authorization)
if (!user) return res.status(401).json({ error: 'Unauthorised' })
return getUserData(user.id) // Use authenticated user ID, not request param

2. External API calls and error handling

Every fetch or axios call needs: a try/catch, a timeout, and a meaningful fallback state. AI tools generate the happy path consistently and the error path inconsistently.

Check: what happens if this API call returns a 500? What happens if it times out? Does the UI reflect the failure state?

3. Database query construction

Any query that incorporates user input must use parameterised queries or an ORM. String interpolation or concatenation into query strings is an injection vulnerability regardless of how benign the input appears in testing.

4. Business logic against your actual requirements

AI models pricing, permissions, and domain logic from general patterns. Your requirements are specific. Describe the exact rule and verify the implementation matches it, not just approximates it.

5. Data exposure on API responses

What does each endpoint return? Is there user data in the response that should not be there? Does the response change based on the requester's permission level, or does it return the same payload regardless?

How to run this review systematically

Manually applying this checklist to every file is slow and inconsistent. A specialist review tool runs all five checks in parallel in seconds and returns severity-graded findings with verified fixes.

The goal is not to replace engineering judgment. It is to catch the deterministic failures so your judgment can focus on the decisions that actually require it.

Set up always-on review in your editor and catch issues before they ship.