AI-Powered Code Reviews: Using Cursor to Improve Your Refactoring Game

I've been experimenting with AI-assisted development tools like Cursor for code reviews and refactoring, and I'm constantly surprised by how much they can teach you about better coding patterns. Rather than just generating code, these tools can become excellent teachers and pair programming partners when used thoughtfully.

Beyond Code Generation: AI as a Learning Tool

Most developers I talk to think of AI coding tools primarily as code generators. While that's useful, I've found the real value comes from using them as intelligent reviewers and teachers. They can spot patterns you might miss, suggest improvements you hadn't considered, and explain the reasoning behind their suggestions.

Practical Code Review with Cursor

Here's how I use Cursor to review and improve code. The key is asking specific questions rather than just requesting generic improvements.

Example 1: Laravel Controller Refactoring

Let's start with a typical Laravel controller that handles user registration:

class UserController extends Controller
{
    public function register(Request $request)
    {
        // Validate input
        $validator = Validator::make($request->all(), [
            'name' => 'required|string|max:255',
            'email' => 'required|string|email|max:255|unique:users',
            'password' => 'required|string|min:8|confirmed',
        ]);

        if ($validator->fails()) {
            return response()->json([
                'errors' => $validator->errors()
            ], 422);
        }

        // Create user
        $user = User::create([
            'name' => $request->name,
            'email' => $request->email,
            'password' => Hash::make($request->password),
            'email_verified_at' => null,
        ]);

        // Send verification email
        Mail::to($user->email)->send(new VerifyEmail($user));

        // Create token
        $token = $user->createToken('auth_token')->plainTextToken;

        return response()->json([
            'user' => $user,
            'token' => $token,
            'message' => 'User registered successfully'
        ], 201);
    }
}

Cursor typically suggests improvements like this:

class UserController extends Controller
{
    public function register(RegisterUserRequest $request)
    {
        try {
            DB::beginTransaction();
            
            $user = $this->userService->createUser($request->validated());
            
            event(new UserRegistered($user));
            
            DB::commit();
            
            return new UserResource($user, [
                'token' => $user->createToken('auth_token')->plainTextToken,
                'message' => 'User registered successfully'
            ]);
            
        } catch (Exception $e) {
            DB::rollback();
            
            Log::error('User registration failed', [
                'email' => $request->email,
                'error' => $e->getMessage()
            ]);
            
            return response()->json([
                'message' => 'Registration failed. Please try again.'
            ], 500);
        }
    }
}

Example 2: JavaScript Function Optimization

Here's a JavaScript function that processes user data:

function processUsers(users) {
    let result = [];
    
    for (let i = 0; i < users.length; i++) {
        if (users[i].active === true) {
            let user = users[i];
            let processedUser = {
                id: user.id,
                name: user.firstName + ' ' + user.lastName,
                email: user.email.toLowerCase(),
                role: user.role || 'user',
                lastLogin: user.lastLogin ? new Date(user.lastLogin).toISOString() : null
            };
            
            if (user.preferences) {
                processedUser.preferences = JSON.parse(user.preferences);
            }
            
            result.push(processedUser);
        }
    }
    
    return result;
}

Cursor suggests a more modern approach:

const processUsers = (users) => {
    if (!Array.isArray(users)) {
        throw new Error('Expected users to be an array');
    }
    
    return users
        .filter(user => user?.active === true)
        .map(user => {
            try {
                return {
                    id: user.id,
                    name: `${user.firstName || ''} ${user.lastName || ''}`.trim(),
                    email: user.email?.toLowerCase() || '',
                    role: user.role || 'user',
                    lastLogin: user.lastLogin 
                        ? new Date(user.lastLogin).toISOString() 
                        : null,
                    preferences: user.preferences 
                        ? JSON.parse(user.preferences) 
                        : {}
                };
            } catch (error) {
                console.warn('Failed to process user:', user.id, error.message);
                return null;
            }
        })
        .filter(Boolean); // Remove any null results
};

Database Query Optimization

One area where AI really shines is identifying potential performance issues in database queries. Here's an example with Laravel Eloquent:

N+1 Query Problem Detection

class BlogController extends Controller
{
    public function index()
    {
        $posts = Post::where('published', true)
                    ->orderBy('created_at', 'desc')
                    ->paginate(10);
        
        return view('blog.index', compact('posts'));
    }
}

// In the Blade template:
@foreach($posts as $post)
    
{{ $post->title }}
    
By {{ $post->author->name }}
    
{{ $post->comments->count() }} comments
    

        @foreach($post->tags as $tag)
            {{ $tag->name }}
        @endforeach
    
@endforeach

AI suggests this optimization:

class BlogController extends Controller
{
    public function index()
    {
        $posts = Post::with([
                        'author:id,name',
                        'tags:id,name',
                    ])
                    ->withCount('comments')
                    ->where('published', true)
                    ->orderBy('created_at', 'desc')
                    ->paginate(10);
        
        return view('blog.index', compact('posts'));
    }
}

// Updated Blade template:
@foreach($posts as $post)
    
{{ $post->title }}
    
By {{ $post->author->name }}
    
{{ $post->comments_count }} comments
    

        @foreach($post->tags as $tag)
            {{ $tag->name }}
        @endforeach
    
@endforeach

Security-Focused Reviews

AI tools are excellent at spotting common security vulnerabilities. Here's an example of how Cursor can help identify potential security issues:

File Upload Vulnerability

public function uploadAvatar(Request $request)
{
    $file = $request->file('avatar');
    
    if ($file) {
        $filename = $file->getClientOriginalName();
        $path = $file->move(public_path('uploads'), $filename);
        
        auth()->user()->update([
            'avatar' => 'uploads/' . $filename
        ]);
        
        return redirect()->back()->with('success', 'Avatar updated!');
    }
    
    return redirect()->back()->with('error', 'No file selected');
}

AI suggests a more secure approach:

public function uploadAvatar(Request $request)
{
    $request->validate([
        'avatar' => 'required|image|mimes:jpeg,png,jpg,gif|max:2048'
    ]);
    
    $file = $request->file('avatar');
    
    // Generate secure filename
    $filename = Str::uuid() . '.' . $file->getClientOriginalExtension();
    
    // Store in private storage
    $path = $file->storeAs('avatars', $filename, 'private');
    
    // Delete old avatar if exists
    if (auth()->user()->avatar) {
        Storage::disk('private')->delete(auth()->user()->avatar);
    }
    
    auth()->user()->update([
        'avatar' => $path
    ]);
    
    return redirect()->back()->with('success', 'Avatar updated successfully!');
}

// Add route to serve private files
public function serveAvatar($filename)
{
    $path = storage_path('app/private/avatars/' . $filename);
    
    if (!file_exists($path)) {
        abort(404);
    }
    
    return response()->file($path);
}

Learning from AI Suggestions

The key to getting value from AI code reviews is asking follow-up questions. Don't just accept suggestions - understand them.

Effective Follow-up Prompts

Building Your Own Patterns

As you work with AI suggestions, you'll start recognizing patterns that work well for your specific use cases. I keep a personal collection of prompts that work well for different types of reviews:

Refactoring Legacy Code

AI tools are particularly helpful when working with legacy code that might not follow current best practices. They can suggest incremental improvements that make code easier to maintain.

Gradual Modernization

Here's a typical legacy PHP function:

function calculate_order_total($order_items, $tax_rate, $discount_code = null) {
    $subtotal = 0;
    foreach($order_items as $item) {
        $subtotal += $item['price'] * $item['quantity'];
    }
    
    $discount = 0;
    if($discount_code != null) {
        if($discount_code == 'SAVE10') {
            $discount = $subtotal * 0.1;
        } elseif($discount_code == 'SAVE20') {
            $discount = $subtotal * 0.2;
        }
    }
    
    $discounted_total = $subtotal - $discount;
    $tax = $discounted_total * $tax_rate;
    $total = $discounted_total + $tax;
    
    return array(
        'subtotal' => $subtotal,
        'discount' => $discount,
        'tax' => $tax,
        'total' => $total
    );
}

AI suggests a more modular approach:

class OrderCalculator 
{
    private array $discountRules = [
        'SAVE10' => 0.1,
        'SAVE20' => 0.2,
    ];
    
    public function calculateTotal(array $orderItems, float $taxRate, ?string $discountCode = null): array
    {
        $subtotal = $this->calculateSubtotal($orderItems);
        $discount = $this->calculateDiscount($subtotal, $discountCode);
        $discountedTotal = $subtotal - $discount;
        $tax = $this->calculateTax($discountedTotal, $taxRate);
        $total = $discountedTotal + $tax;
        
        return [
            'subtotal' => round($subtotal, 2),
            'discount' => round($discount, 2),
            'tax' => round($tax, 2),
            'total' => round($total, 2),
        ];
    }
    
    private function calculateSubtotal(array $orderItems): float
    {
        return array_reduce($orderItems, function ($carry, $item) {
            return $carry + ($item['price'] * $item['quantity']);
        }, 0);
    }
    
    private function calculateDiscount(float $subtotal, ?string $discountCode): float
    {
        if (!$discountCode || !isset($this->discountRules[$discountCode])) {
            return 0;
        }
        
        return $subtotal * $this->discountRules[$discountCode];
    }
    
    private function calculateTax(float $amount, float $taxRate): float
    {
        return $amount * $taxRate;
    }
}

Testing AI-Suggested Code

Always test AI suggestions thoroughly. While AI is great at identifying patterns and potential improvements, it doesn't always understand your specific business context or edge cases.

Verification Strategies

Building Your AI Review Workflow

Here's the workflow I've developed for using AI tools effectively in code reviews:

Team Integration

If you're working on a team, consider these approaches for sharing AI insights:

Limitations and Considerations

AI code review tools are powerful, but they're not perfect. Here are some limitations to keep in mind:

The Learning Mindset

The biggest benefit of using AI for code reviews isn't the immediate improvements - it's the patterns and techniques you learn over time. Each suggestion is an opportunity to understand why certain approaches work better than others.

I've found that my own code quality has improved significantly just from seeing consistent AI feedback on common issues. It's like having a patient mentor who never gets tired of explaining why certain patterns are better.