How to Review AI-Generated Code in Laravel Projects

If you have started using AI to generate Laravel code, you are not alone. Developers everywhere are using tools like Claude, ChatGPT, and Cursor to scaffold controllers, write Eloquent queries, and build front-end components at blazing speed. But there is a catch: AI-generated code looks plausible but often contains subtle bugs, security holes, and architectural missteps. This guide gives you a practical checklist for reviewing AI-generated Laravel code so you catch problems before they reach production.

The AI Code Review Mindset

Treat AI output like a junior developer's first draft. It will get the shape right but miss edge cases, best practices, and security considerations. Your job as the reviewer is to fill those gaps. Never trust AI-generated code without reviewing it — even if it compiles and passes your test suite on the first try.

1. Security: The Most Critical Check

Security vulnerabilities are the most dangerous class of AI code issues. LLMs are trained on public code, including insecure examples, and they do not inherently understand your application's security context.

Mass Assignment Protection

AI frequently generates controllers that pass all request input directly to mass assignment:

<?php

// AI-generated code — DANGEROUS
public function store(Request $request)
{
    return User::create($request->all()); // Never do this
}

// Fixed version
public function store(CreateUserRequest $request)
{
    return User::create($request->validated());
}

Always check that AI-generated code uses form requests with $request->validated() or explicitly lists fillable fields rather than blindly passing all input.

SQL Injection in Raw Queries

AI sometimes falls back to raw SQL with string interpolation instead of using Eloquent or the query builder:

<?php

// AI-generated code — SQL injection risk
DB::select("SELECT * FROM users WHERE email = '$email'");

// Fixed version
DB::table('users')->where('email', $email)->get();

Authorization Checks

Verify that AI-generated controller methods include authorization gates or policies. AI often forgets to add $this->authorize() calls, leaving endpoints open to any authenticated (or unauthenticated) user.

<?php

// AI might generate this without authorization
public function destroy(Post $post)
{
    $post->delete();
    return redirect()->back();
}

// Always verify this is present
public function destroy(Post $post)
{
    $this->authorize('delete', $post);
    $post->delete();
    return redirect()->back();
}

Environment Variables and Secrets

AI has a habit of hardcoding API keys, database credentials, or other secrets directly in code. Search the generated code for any literal strings that look like credentials, tokens, or keys.

2. Data Integrity and Validation

One of the most common complaints about AI-generated code — echoed in community discussions — is that it ignores data integrity. Here is what to watch for.

Missing Validation Rules

AI might generate a form request with minimal or no validation rules:

<?php

// AI might generate this — too permissive
public function rules(): array
{
    return [
        'email' => 'required|email',
        'password' => 'required',
    ];
}

// What you probably need
public function rules(): array
{
    return [
        'email' => 'required|email|unique:users,email',
        'password' => 'required|min:12|confirmed',
    ];
}

Missing Database Constraints

Check AI-generated migrations carefully. AI often omits foreign key constraints, unique indexes, or cascading deletes that your data model requires:

<?php

// AI might generate this — missing constraints
Schema::create('posts', function (Blueprint $table) {
    $table->id();
    $table->string('title');
    $table->text('body');
    $table->unsignedBigInteger('user_id');
    $table->timestamps();
});

// What it should look like
Schema::create('posts', function (Blueprint $table) {
    $table->id();
    $table->string('title');
    $table->text('body');
    $table->foreignId('user_id')->constrained()->cascadeOnDelete();
    $table->timestamps();
});

Race Conditions and Atomicity

When generating code that handles money, inventory, or any resource where concurrent access matters, AI often forgets to use database transactions or atomic locks:

<?php

// AI-generated code — race condition
public function purchase(Request $request)
{
    $product = Product::find($request->product_id);
    if ($product->stock < $request->quantity) {
        return back()->with('error', 'Out of stock');
    }
    $product->decrement('stock', $request->quantity);
    // Order created here...
}

// Fixed with atomic lock
public function purchase(Request $request)
{
    $product = Product::lockForUpdate()->find($request->product_id);
    // ... rest of logic inside a DB::transaction()
}

3. N+1 Query Problems

AI frequently generates Eloquent queries without eager loading, leading to N+1 performance problems that only show up under load:

<?php

// AI-generated — N+1 waiting to happen
$posts = Post::all();
foreach ($posts as $post) {
    echo $post->author->name; // One query per post
}

// Fixed — eager load
$posts = Post::with('author', 'tags', 'comments')->get();

Use Laravel Debugbar or Clockwork during review to see the actual query count. If the AI-generated code produces dozens of queries for a single page load, it needs eager loading or a cached query.

4. Architectural Consistency

AI does not know your project's conventions. Review generated code for:

Naming conventions: Does it use camelCase for methods? Singular for model names? Does it follow your existing pattern for controller method names?
Service layer usage: Does it bypass your service/repository layer and put business logic directly in controllers?
Route model binding: Does it manually fetch models by ID instead of using implicit route model binding?
Custom casts and accessors: Does it manually format attributes that your model already handles via $casts or accessors?

<?php

// AI-generated — ignores route model binding
Route::get('/posts/{id}', function ($id) {
    $post = Post::findOrFail($id);
    return view('posts.show', compact('post'));
});

// What you probably already have
Route::get('/posts/{post}', function (Post $post) {
    return view('posts.show', compact('post'));
});

5. Testing the AI Output

Here is a systematic review workflow for AI-generated Laravel code:

Run the test suite before and after adding AI code to confirm nothing broke.
Check for test coverage — if AI generated a feature, it should have generated tests too. Review those tests for completeness.
Manually test edge cases: empty inputs, duplicate submissions, unauthenticated access, expired tokens.
Review the diff carefully — look at every line changed, not just the AI-generated additions.
Run Laravel-specific static analysis:
- php artisan route:list — verify routes use the correct middleware
- php artisan model:show User — verify model configuration
- vendor/bin/phpstan analyse — catch type errors

6. Tools That Help

Laravel Pulse — monitor for slow queries and N+1 issues after deployment
Laravel Debugbar — inspect queries, routes, and middleware during development
PHPStan or Psalm — static analysis catches type mismatches and undefined methods
Rector — automates refactoring to match your project's coding standards
Pint or PHP CS Fixer — enforce consistent code style automatically

Conclusion

AI code generation is a massive productivity boost, but it shifts your role from writer to reviewer. The same way you would never deploy a junior developer's code without a thorough review, you should never deploy AI-generated code without checking it. Focus your review on security, data integrity, query performance, and architectural consistency. Build a review checklist tailored to your project and stick to it.

As one developer on Laracasts recently put it: "I felt like a superhero having AI write code at blazing speed. But now I keep running into issues with data integrity." The solution is not to stop using AI — it is to get better at reviewing what it produces.