The Future of Coding: Be the Architect, Not Just the Coder

Time has changed, and so should the way we learn to code. Tools like Cursor, Copilot, and Windsurf are incredible - they can often produce code that's more polished than what even seasoned developers might write. Sometimes they deliver code that's sleek and professional, and other times, they might churn out something that feels a bit rough around the edges.

That's the catch - you need to know what great code looks like to guide AI in writing it for you. You'll likely have to refine its output multiple times. It's essential to provide clear context: explain what you're building and how you envision your code evolving.

Being the Quality Judge

If you instruct the AI correctly, the results can be quite surprising. For instance, I recently tried generating the logic for a traditional snake game (the legendary Nokia Snake game). I provided the requirements, and the AI produced code something like this

class SnakeGame {
    constructor(boardWidth, boardHeight) {
        this.boardWidth = boardWidth;
        this.boardHeight = boardHeight;

        // Initialize the snake in the center of the board with size 3
        this.snake = [//initizing points];

    }

    // Check if the game is over
    isGameOver() {
        return this.gameOver;
    }

    // Helper function to check if the snake hits itself
    isSnakeCollision(newHead) {
        return this.snake.some(segment => segment.x === newHead.x && segment.y === newHead.y);
    }

    // Moves the snake based on the direction
    moveSnake(snakeDirection) {
        // Moving logic
    }

    // Display the current state of the board and snake
    printBoard() {
        // Printing the board
    }
}

// Example usage
const game = new SnakeGame(10, 10);

// Simulate a game loop
const directions = ['RIGHT', 'DOWN', 'LEFT', 'UP'];
let index = 0;

while (!game.isGameOver()) {
    game.moveSnake(directions[index % directions.length]);
    game.printBoard();
    index++;
}

At first glance, this code might seem to work - it’s simple and functional. However, it clearly has the hallmarks of an amateur effort, much like what you’d expect from a college undergraduate. Some basic issues with the code are Lack of Proper Class Management, No Separation of Concerns, No Adherence to SOLID Principles, General Code Quality etc.

Then I explained to the context to AI, Okay, this code works, but it's not what I'm after. My future requirements include changes like food generation and collision logic modifications, and as the game grows more complex, this one-block code will turn into a tangled mess. In real-world development, multiple teams work on a big product -one team handles one part of the API while another takes care of something else. Everything needs to be loosely coupled so that updates in one area don't break everything else.

After a few iterations, the final output looked much better.

Somewhat like this

public class SnakeGame {
    int rows = 20;
    int cols = 20;
    int[][] board;
    // Storing the snake, food, collision checks, and more all here...
    
    public static void main(String[] args) {
        // A massive main with all logic inside...
    }
}


public final class Position {
    private final int row;
    private final int col;
    // ...
}

public enum Direction {
    UP, DOWN, LEFT, RIGHT
}

public class Snake {
    private final Deque<Position> body = new LinkedList<>();
    private Direction currentDirection;
    // ...
}

private List<Position> body = new ArrayList<>();

public void move() {
    body.add(0, newHead);        // insertion at index 0: expensive
    body.remove(body.size() - 1);
}

public class Board {
    private final int rows;
    private final int cols;
    private final Snake snake;
    private final Set<Position> foodPositions = new HashSet<>();
    // ...
}

public interface FoodSpawner {
    Position getNextFoodPosition(Board board);
}

public class RandomFoodSpawner implements FoodSpawner {
    // ...
}

public class CollisionDetector {
    public boolean isCollision(Board board, Snake snake, Position nextHead) {
        // ...
    }
}

public class GameController {
    private final Board board;
    private final FoodSpawner foodSpawner;
    private final CollisionDetector collisionDetector;
    private boolean gameOver = false;
    // ...
}

public class SnakeGameMain {
    public static void main(String[] args) {
        // ...
    }
}

Why is this version better?

At a high level, this final version shines because it’s organized into clear, dedicated components. It has clear Separation of Responsibilities, Code Modularity, This approach mimics real-world software development practices where teams work on different modules and Components interact through well-defined interfaces, meaning changes in one area have minimal impact on others.

Thats the point, you need to know exactly what you're aiming to build and be well-versed in core software development principles. But you don't have to write every line of code yourself. Instead, you need to be a good judge of the AI’s output.

bdw if have not already follow me on twitter/X

Use AI, But Stay in the Driver’s Seat

AI can be an incredible coding assistant, but don’t just take its output at face value. It’s fast, but sometimes it takes shortcuts, ignores best practices, or produces amateur-level code that you’d never ship in a real-world product.

Using AI for coding is like having a robot chef. It can chop, mix, and cook at lightning speed, but it doesn’t know if the dish actually tastes good. If you let it run wild, you might end up with an overcooked, flavorless mess. But if you guide it - adjust the seasoning, refine the recipe - you get a meal worth serving.

Some real world examples

Example 1: Authentication & Authorization

You tell AI:👉 "Generate an authentication module with JWT."

It spits out a working login system, but you notice something: no password hashing, no token expiration, and no refresh mechanism. AI took the shortcut - it wrote code that works but isn’t secure.

Your job? Spot the flaws and make AI fix them:👉 "Make sure passwords are hashed using bcrypt, enforce token expiration, and add refresh tokens for better security."

Now, AI produces a more secure and industry-standard implementation. You didn’t write the whole thing yourself, but you ensured it was done right.

Example 2: RESTful API Design

You ask AI to generate a CRUD API for user management. It does the job:

app.post("/users", async (req, res) => {
    const user = await db.insert(req.body);
    res.status(201).json(user);
});

Looks fine, but hold on - where’s input validation? Error handling? Authentication? AI went the lazy route. If you ship this as-is, you’re asking for security nightmares and buggy behavior.

So you guide AI:👉 "Add input validation to prevent bad data, include proper error handling, and ensure that only authenticated users can create new accounts."

Now AI rewrites it, making it production-ready. Again, you didn’t have to write the code, but you made sure it followed best practices.

Example 3: Scalable Architecture

You're building a backend system and ask AI for a microservices-based architecture. It generates something, but when you review it, you see:❌ Hard-coded service dependencies❌ No message queue for communication❌ No fault tolerance

If you blindly accept this, your system will collapse under real-world load. So you push AI further:👉 "Make the services loosely coupled, introduce message queues for better communication, and add retry mechanisms for failures."

Now the AI-generated architecture is not just functional, but scalable and maintainable.

These are just basic scenarios. The takeaway is this: you need to know software engineering principles well.

AI is Your Tool - You Are the Engineer

Imagine a complete non-coder trying to build an app with AI. Sure, the AI will generate working code, and they might even get an app running. But here’s the problem - how the hell will they know if the code is good?

• What if the AI wrote an insecure authentication system that exposes user data?

• What if the database queries are horribly inefficient and will crash when traffic increases?

• What if the entire architecture is a mess, making future updates a nightmare?

AI doesn't think ahead (not yet) - it just gives you a functional output based on what you ask. It won’t warn you about maintainability, security risks, or performance issues unless you explicitly tell it to.

So yes, AI can build an app. But is the coding standard good? Is the architecture scalable? Will the app survive real-world use? These are the questions only you, as the engineer, can answer.

Let AI do the grunt work, but you stay in control. It will sometimes take shortcuts, write messy code, or miss crucial details. Your job is to catch those weaknesses, refine its output, and push it toward writing a version that meets real-world standards.

This is why your focus shouldn’t be on memorizing syntax or frameworks anymore - you don’t need to out-code AI. You need to out-think it. Learn what good software looks like so you can guide AI to write high-quality code, not just "working" code. Because in the real world, working isn’t enough - your software needs to be secure, scalable, and maintainable.

Software development is bound to change - it's inevitable. Your role won't be to become an expert in every technology but rather to know how to judge the technical quality of a product.

You can't compete with AI on knowledge, but you can harness it to build high-quality solutions.