You’re staring at a pull request filled with AI-generated code that passes all tests, looks syntactically correct, and even follows your team’s formatting guidelines. Everything seems perfect, but something feels… off. Three weeks later, you’re debugging a production issue at 2 AM, tracing it back to that exact code.

I’ve been there more times than I’d like to admit. After reviewing hundreds of AI-generated pull requests over the past year, I’ve noticed that AI assistants have their own unique “tells” — subtle patterns that often lead to problems down the road. These aren’t syntax errors or obvious bugs that your IDE will catch. They’re deeper architectural smells that can slip through code review and cause headaches in production.

Here are the eight patterns I’ve learned to watch for when reviewing AI-generated code.

The Silent Error Swallower

AI loves to handle errors, but it often does so in the most polite way possible — by quietly catching everything and moving on.

def process_user_data(user_id):
    try:
        user = fetch_user(user_id)
        result = complex_calculation(user.data)
        save_result(user_id, result)
        return result
    except:
        return None

This looks reasonable at first glance, but that bare except clause is hiding a world of potential issues. Network timeouts, database connection errors, and calculation failures all get the same treatment: silence.

I’ve found that AI-generated code often treats error handling as a checkbox to tick rather than a meaningful part of the application flow. The fix is usually straightforward — be explicit about what errors you’re catching and how you’re handling them.

The Over-Eager Optimizer

AI assistants love to show off their knowledge of optimization techniques, sometimes applying them in situations where they create more problems than they solve.

// AI-generated memoization that seemed helpful
const memoizedResults = new Map();

function calculateUserScore(userId) {
    if (memoizedResults.has(userId)) {
        return memoizedResults.get(userId);
    }
    
    const score = expensiveCalculation(userId);
    memoizedResults.set(userId, score);
    return score;
}

The issue here isn’t the memoization itself — it’s that this cache grows indefinitely and never considers that user scores might change. In production, this led to stale data issues and a slow memory leak.

AI often applies optimization patterns without considering the full context of how the code will be used. Always question whether the optimization is actually needed and if it introduces new complexity.

The Configuration Hardcoder

This one drives me crazy. AI will generate code that works perfectly in your development environment but assumes too much about the production setup.

def upload_file(filename):
    # AI assumed development setup
    bucket = "my-dev-bucket"
    region = "us-east-1"
    
    s3_client = boto3.client('s3', region_name=region)
    return s3_client.upload_file(filename, bucket, f"uploads/{filename}")

The AI correctly understood the S3 upload pattern but hardcoded values that only work in one environment. This type of code smell often appears when AI focuses too narrowly on making the immediate code work without considering deployment flexibility.

The Assumption Validator

AI-generated code often includes validation that looks thorough but makes dangerous assumptions about data structure or user behavior.

function processOrder(order) {
    // Validates the obvious stuff
    if (!order || !order.items || order.items.length === 0) {
        throw new Error("Invalid order");
    }
    
    // But assumes items always have these properties
    const total = order.items.reduce((sum, item) => {
        return sum + (item.price * item.quantity);
    }, 0);
    
    return processPayment(total);
}

The validation catches empty orders but doesn’t verify that price and quantity exist or are numbers. I’ve seen this pattern cause NaN values to propagate through financial calculations — not fun to debug in production.

The One-Size-Fits-All Handler

AI loves to create generic solutions, even when specific handling would be more appropriate.

def handle_api_response(response):
    if response.status_code != 200:
        # Generic handling for all non-200 responses
        logger.error(f"API error: {response.status_code}")
        return {"error": "API request failed"}
    
    return response.json()

This treats a 404 (resource not found) the same as a 500 (server error) or 429 (rate limited). Each of these scenarios should probably be handled differently — retrying on 429, treating 404 as expected behavior, and escalating 500s to monitoring alerts.

The Synchronous Sleeper

When AI needs to wait for something, it often reaches for the simplest solution without considering the broader impact.

def wait_for_job_completion(job_id):
    while True:
        status = check_job_status(job_id)
        if status == "completed":
            return get_job_result(job_id)
        if status == "failed":
            raise Exception("Job failed")
        
        time.sleep(5)  # The problematic line

This blocking loop with time.sleep() will tie up a thread (or the entire event loop in Node.js) while waiting. In a web application, this could quickly exhaust your server’s capacity to handle other requests.

The Context-Blind Implementer

AI sometimes implements patterns correctly in isolation but misses how they fit into the larger system architecture.

// AI implemented a perfect singleton pattern
class DatabaseConnection {
    constructor() {
        if (DatabaseConnection.instance) {
            return DatabaseConnection.instance;
        }
        
        this.connection = createConnection();
        DatabaseConnection.instance = this;
    }
}

The singleton pattern is implemented correctly, but in a microservices architecture with horizontal scaling, this shared connection can become a bottleneck. The AI focused on the pattern itself without considering the deployment context.

The Test-Driven Tunnel Vision

This last one is subtle but important. AI often writes code that passes the specific tests you’ve shown it but misses edge cases that weren’t in your test examples.

def parse_user_input(input_str):
    # Works for the test cases: "yes", "no", "true", "false"
    if input_str.lower() in ["yes", "true", "1"]:
        return True
    elif input_str.lower() in ["no", "false", "0"]:
        return False
    else:
        return False  # Default to False for anything else

The logic handles the test cases perfectly, but what about empty strings, None values, or whitespace? The AI optimized for passing tests rather than robust input handling.

Building Better Code Review Habits

Spotting these patterns has made me a better reviewer of both AI-generated and human-written code. The key is to look beyond “does this code work?” and ask “how will this code behave in production?”

When reviewing AI-generated code, I now spend extra time on error handling, configuration management, and performance implications. I also try to run the code through mental scenarios that weren’t in the original prompt or test cases.

The goal isn’t to avoid AI assistance — these tools have made me significantly more productive. Instead, it’s about developing pattern recognition skills that help you guide AI toward better solutions and catch potential issues before they reach production.

Next time you’re reviewing AI-generated code, try looking for these eight patterns. You might be surprised at what you find hiding in plain sight.