As developers, we often find ourselves stuck doing the same repetitive tasks—writing boilerplate code, generating test cases, refactoring functions, or even documenting APIs. While these tasks are essential, they can be time-consuming and drain productivity. This is where AI-powered coding tools come in.

With the rise of AI assistants like GitHub Copilot, ChatGPT, and other AI-driven IDE extensions, developers now have the ability to automate repetitive coding tasks, speed up workflows, and focus more on solving complex problems. Instead of spending hours rewriting similar functions, you can delegate those tasks to AI and reserve your energy for building the features that truly matter.

In this tutorial, we’ll explore how to leverage AI to make your coding process smarter and faster. You’ll learn practical examples of how AI can generate boilerplate code, automate documentation, assist with unit tests, and even refactor code for better readability and performance. By the end, you’ll see how AI can act as your pair programmer, helping you boost productivity and reduce tedious coding work.

Why Automate Repetitive Coding Tasks?

Every developer has faced the frustration of writing the same type of code over and over again. Whether it’s scaffolding boilerplate, setting up CRUD operations, or writing endless test cases, these tasks consume a significant chunk of development time without adding much innovation.

1. Common Repetitive Tasks in Software Development

Here are a few examples where repetition creeps into our workflows:

Boilerplate Code: Setting up controllers, models, routes, and services in frameworks like Express.js, Spring Boot, or Django.
CRUD Operations: Create, Read, Update, and Delete functions are necessary in almost every application.
Unit & Integration Tests: Writing similar test patterns for multiple functions or modules.
Documentation: Generating function docstrings, API reference docs, or code comments.
Refactoring: Cleaning up duplicated code, renaming variables, and restructuring functions.

Example:
Imagine you’re building a REST API with Node.js + Express. For every new resource (e.g., users, products, orders), you’ll likely write nearly identical routes and controller methods. That’s hours of work that could be automated.

// Example: Typical repetitive CRUD in Express.js
app.get('/users', getUsers);
app.post('/users', createUser);
app.put('/users/:id', updateUser);
app.delete('/users/:id', deleteUser);

This structure is necessary but repetitive. AI tools can generate this for you instantly, saving time and reducing mistakes.

2. The Cost of Manual Repetition

Time-Consuming: Hours wasted on code you’ve written dozens of times before.
Error-Prone: Humans are more likely to make mistakes when repeating tedious tasks.
Developer Burnout: Boring, repetitive work lowers motivation and productivity.

3. Benefits of Automating with AI

Automating repetitive coding tasks with AI can:

Save Time: Generate boilerplate and tests in seconds.
Improve Accuracy: Reduce human errors with consistent patterns.
Boost Productivity: Focus more on solving core problems instead of busywork.
Accelerate Learning: AI can suggest best practices and patterns as it generates code.

Example with AI:
Instead of manually writing repetitive test cases, you can ask an AI tool:

“Generate Jest unit tests for this function that validates user input.”

In seconds, you’ll get a structured test suite that you can refine, rather than starting from scratch.

✅ By automating these repetitive tasks, you not only work faster but also ensure consistency and maintainability across your codebase.

Choosing the Right AI Tools for Developers

Before diving into automation, it’s important to know which tools can actually help you in your workflow. Today, developers have access to a variety of AI-powered assistants that can integrate seamlessly into their coding environments.

1. Popular AI Tools for Coding

GitHub Copilot
- Powered by OpenAI’s Codex model.
- Integrates directly into VS Code, JetBrains, and Neovim.
- Suggests code completions, generates boilerplate, and even writes functions based on comments.
ChatGPT
- Accessible via web app, API, or IDE plugins.
- Great for code explanations, bug fixing, generating test cases, or refactoring.
- Useful beyond coding—can help with documentation, prompts, or architectural advice.
Tabnine
- AI-powered autocompletion tool.
- Works with multiple programming languages and IDEs.
- Focuses on privacy and on-device models for teams that want more control over code data.
Amazon CodeWhisperer
- Similar to GitHub Copilot.
- Integrates with AWS services, making it especially useful for cloud developers.
IDE Extensions (VS Code, JetBrains, IntelliJ, etc.)
- Many modern IDEs have built-in or third-party AI assistants.
- These tools often provide real-time suggestions, inline documentation, and quick refactoring help.

2. Factors to Consider When Choosing an AI Coding Tool

When picking the right AI tool, ask yourself these questions:

Language Support
- Does the tool support your main programming language (JavaScript, Python, Java, Rust, etc.)?
IDE Integration
- Will it work smoothly in your preferred IDE (VS Code, JetBrains, IntelliJ, Neovim)?
Privacy & Security
- Is your code sent to external servers, or can it run locally?
- Important for companies with strict data policies.
Pricing
- Free tiers are available for most tools, but advanced features often require paid subscriptions.
Team Collaboration
- Does it support shared suggestions, team training, or enterprise-level integration?

3. Example Setup

If you’re a JavaScript/Node.js developer using VS Code, a common setup could be:

GitHub Copilot → for inline code suggestions and boilerplate generation.
ChatGPT plugin for VS Code → for asking natural language queries like “Write unit tests for this function.”
Prettier + ESLint → to ensure AI-generated code follows your style guide.

This combo ensures that while AI generates code, you still maintain consistency and quality across your project.

✅ With the right AI tools in place, you’ll be ready to start automating tasks like boilerplate generation, testing, refactoring, and documentation.

Automating Boilerplate Code

Boilerplate code refers to the repetitive, standard code structures that developers write again and again when setting up new projects. While necessary, boilerplate can be time-consuming and uninspiring. AI tools can help by instantly generating scaffolding code, so you can focus on logic instead of setup.

1. Common Boilerplate Scenarios

Setting up CRUD routes in web frameworks (Express.js, Spring Boot, Django).
Defining models and DTOs (Data Transfer Objects).
Writing configuration files (e.g., database connections, authentication setup).
Repeating React/Angular/Vue components with a similar structure.

2. Example: Express.js CRUD Boilerplate

Let’s say you’re building a REST API for managing products. Normally, you would write repetitive route and controller functions like this:

// routes/productRoutes.js
const express = require('express');
const router = express.Router();
const productController = require('../controllers/productController');

router.get('/', productController.getAllProducts);
router.get('/:id', productController.getProductById);
router.post('/', productController.createProduct);
router.put('/:id', productController.updateProduct);
router.delete('/:id', productController.deleteProduct);

module.exports = router;

And then you’d need to write a matching controller file with a similar structure for each resource.

3. Using AI to Generate Boilerplate

Instead of writing this manually, you can use GitHub Copilot or ChatGPT. For example, in VS Code with Copilot, simply type a comment:

// Create Express routes and controller functions for Product CRUD

Copilot will suggest a complete block of code with all routes and controller stubs filled in.

How to Use AI to Automate Repetitive Coding Tasks - auto suggestion copilot

If you’re using ChatGPT, you could ask:

“Generate Express.js CRUD routes and controllers for a Product model with fields: name, price, and description.”

AI will instantly generate a full boilerplate like:

// Create Express routes and controller functions for Product CRUD
const express = require("express");
const router = express.Router();
const Product = require("./models/Product"); // Assuming you have a Product model defined

// Create a new product - POST /products
router.post("/products", async (req, res) => {
  try {
    const product = new Product(req.body);
    await product.save();
    res.status(201).json(product);
  } catch (error) {
    res.status(400).json({ error: error.message });
  }
});

// Get all products - GET /products
router.get("/products", async (req, res) => {
  try {
    const products = await Product.find();
    res.status(200).json(products);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

// Get a single product by ID - GET /products/:id
router.get("/products/:id", async (req, res) => {
  try {
    const product = await Product.findById(req.params.id);
    if (!product) {
      return res.status(404).json({ error: "Product not found" });
    }
    res.status(200).json(product);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

// Update a product by ID - PUT /products/:id
router.put("/products/:id", async (req, res) => {
  try {
    const product = await Product.findByIdAndUpdate(req.params.id, req.body, {
      new: true,
      runValidators: true
    });
    if (!product) {
      return res.status(404).json({ error: "Product not found" });
    }
    res.status(200).json(product);
  } catch (error) {
    res.status(400).json({ error: error.message });
  }
});

// Delete a product by ID - DELETE /products/:id
router.delete("/products/:id", async (req, res) => {
  try {
    const product = await Product.findByIdAndDelete(req.params.id);
    if (!product) {
      return res.status(404).json({ error: "Product not found" });
    }
    res.status(200).json({ message: "Product deleted successfully" });
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

module.exports = router;

4. Benefits of AI-Generated Boilerplate

Speed: Scaffolding that normally takes 15–30 minutes is generated in seconds.
Consistency: Code follows the same structure across all resources.
Focus: You can spend more time on implementing business logic instead of setting up.

✅ With AI handling boilerplate, you can quickly move on to writing meaningful code that adds value to your application.

AI for Writing Unit Tests

Unit tests are essential for ensuring code quality, but writing them often feels repetitive and tedious. Developers spend a lot of time creating similar test cases for different functions. AI can speed this up by generating test templates or even full test cases that you can refine.

1. Why Automate Test Writing?

Repetition: Many test cases follow similar patterns (setup → execution → assertion).
Time-Consuming: For large projects, writing tests can take longer than writing the actual feature.
Coverage Gaps: Developers sometimes skip tests due to a lack of time—AI helps close this gap.

2. Example 1: Generating Jest Tests (JavaScript)

Suppose you have a simple utility function:

// utils/math.js
function add(a, b) {
  return a + b;
}

module.exports = add;

Normally, you’d need to write a test file like this:

// tests/math.test.js
const add = require('../utils/math');

test('adds two numbers', () => {
  expect(add(2, 3)).toBe(5);
});

With AI assistance (e.g., GitHub Copilot or ChatGPT), you can simply type:

// Write Jest tests for the add function

And AI can generate multiple cases instantly:

const add = require('../utils/math');

describe('add function', () => {
  test('should add two positive numbers', () => {
    expect(add(2, 3)).toBe(5);
  });

  test('should add negative numbers', () => {
    expect(add(-2, -3)).toBe(-5);
  });

  test('should add a positive and a negative number', () => {
    expect(add(5, -3)).toBe(2);
  });

  test('should return NaN if input is not a number', () => {
    expect(add(5, 'a')).toBeNaN();
  });
});

3. Example 2: Generating JUnit Tests (Java)

For Java developers, let’s take a simple class:

// Calculator.java
public class Calculator {
    public int add(int a, int b) {
        return a + b;
    }
}

Instead of writing JUnit tests from scratch, you could ask AI:

“Generate JUnit 5 tests for Calculator.add method.”

AI might return:

import static org.junit.jupiter.api.Assertions.assertEquals;
import org.junit.jupiter.api.Test;

public class CalculatorTest {

    Calculator calculator = new Calculator();

    @Test
    void testAddPositiveNumbers() {
        assertEquals(5, calculator.add(2, 3));
    }

    @Test
    void testAddNegativeNumbers() {
        assertEquals(-5, calculator.add(-2, -3));
    }

    @Test
    void testAddMixedNumbers() {
        assertEquals(2, calculator.add(5, -3));
    }
}

4. Benefits of AI-Assisted Test Generation

Increased Test Coverage: AI can suggest edge cases you might overlook.
Faster Development: Spend less time writing boilerplate tests.
Better Reliability: Catch potential bugs earlier with more complete tests.

✅ By using AI to scaffold unit tests, you save time while improving your project’s overall code quality. You can then focus on refining tests for complex scenarios.

Refactoring and Code Optimization with AI

Refactoring is the process of improving code structure without changing its functionality. It makes your code more readable, maintainable, and efficient. But refactoring can be tedious—especially when you need to clean up legacy code or optimize complex functions. AI tools can help automate much of this process.

1. Why Use AI for Refactoring?

Readability: AI can simplify messy or overly complex code.
Consistency: Suggests standardized naming conventions and patterns.
Performance: Identifies potential optimizations (e.g., reducing loops, using built-in methods).
Safety Net: You can quickly generate alternative implementations and compare results.

2. Example 1: Simplifying a JavaScript Function

Here’s a messy function for finding duplicates in an array:

function findDuplicates(arr) {
  let duplicates = [];
  for (let i = 0; i < arr.length; i++) {
    for (let j = i + 1; j < arr.length; j++) {
      if (arr[i] === arr[j] && !duplicates.includes(arr[i])) {
        duplicates.push(arr[i]);
      }
    }
  }
  return duplicates;
}

With an AI assistant, you could ask:

“Refactor this function to make it cleaner and more efficient.”

AI might return:

function findDuplicates(arr) {
  const seen = new Set();
  const duplicates = new Set();

  for (const item of arr) {
    if (seen.has(item)) {
      duplicates.add(item);
    } else {
      seen.add(item);
    }
  }

  return [...duplicates];
}

✅ The refactored code is more efficient (O(n) instead of O(n²)) and easier to understand.

3. Example 2: Improving Java Code

Suppose you have this Java method:

public String formatName(String firstName, String lastName) {
    if (firstName == null) {
        firstName = "";
    }
    if (lastName == null) {
        lastName = "";
    }
    return firstName.trim() + " " + lastName.trim();
}

You could ask AI to refactor and make it more concise. AI might return:

public String formatName(String firstName, String lastName) {
    return String.format("%s %s", 
        firstName == null ? "" : firstName.trim(),
        lastName == null ? "" : lastName.trim()
    ).trim();
}

✅ This version is shorter, handles nulls gracefully, and is easier to read.

4. Benefits of AI-Powered Refactoring

Cleaner Code: Better readability and maintainability.
Optimized Performance: More efficient algorithms suggested.
Consistency: AI enforces common coding standards automatically.
Time-Saving: Refactor legacy or repetitive code in minutes instead of hours.

⚡ With AI’s help, you can maintain high-quality, optimized codebases without spending endless hours manually cleaning up.

Automating Documentation with AI

Documentation is one of the most important yet often neglected parts of software development. Developers love writing code but rarely enjoy documenting it. Fortunately, AI can automate much of the documentation process, from generating function docstrings to creating API references.

1. Why Automate Documentation?

Time-Saving: No need to manually write repetitive comments or API descriptions.
Consistency: Standardized documentation style across your codebase.
Better Onboarding: Helps new team members understand code faster.
Up-to-Date Docs: Easier to maintain documentation as code evolves.

2. Example 1: Generating Docstrings (Python)

Suppose you have this Python function:

def calculate_discount(price, discount):
    return price - (price * discount)

If you ask AI:

“Add a proper Python docstring to this function.”

You might get:

def calculate_discount(price, discount):
    """
    Calculate the discounted price.

    Args:
        price (float): The original price of the item.
        discount (float): The discount rate (e.g., 0.1 for 10%).

    Returns:
        float: The final price after applying the discount.
    """
    return price - (price * discount)

✅ Now the function is self-documented, making it easier for others to use.

3. Example 2: Generating API Documentation (JavaScript / Express.js)

If you have an Express route like this:

app.post('/users', (req, res) => {
  // Create new user
});

You could ask AI:

“Generate Swagger/OpenAPI documentation for this endpoint.”

AI might provide:

paths:
  /users:
    post:
      summary: Create a new user
      description: Creates a new user in the system with the provided details.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              properties:
                name:
                  type: string
                email:
                  type: string
                password:
                  type: string
      responses:
        "201":
          description: User created successfully
        "400":
          description: Invalid input

✅ With minimal effort, you now have machine-readable API docs that can be used with Swagger UI or Postman.

4. Example 3: Generating README.md Files

For a new project, you could simply provide your codebase or project description to AI and ask:

“Generate a README.md with installation, usage, and contribution instructions.”

AI would scaffold a professional-looking README file, saving hours of manual writing.

5. Benefits of AI-Generated Documentation

Saves time and effort on tedious documentation tasks.
Improves team communication by keeping docs clear and structured.
Keeps documentation aligned with the latest code changes.
Makes projects more professional when shared publicly.

✅ With AI handling documentation, you can keep your codebase well-documented without sacrificing productivity.

Best Practices for Using AI in Coding

AI can be a powerful ally in software development, but like any tool, it should be used wisely. Blindly copying and pasting AI-generated code may introduce bugs, security vulnerabilities, or performance issues. To maximize the benefits of AI while minimizing risks, follow these best practices:

1. Always Review AI-Generated Code

AI-generated code should never be trusted blindly. Treat it like code from a junior developer—it can be helpful but requires review.

Check for logic errors.
Ensure the code follows your project’s coding standards.
Confirm that it aligns with your security requirements.

2. Use AI for Repetition, Not Creativity

AI excels at generating boilerplate, repetitive patterns, and documentation—but it’s not great at making architectural decisions.

✅ Use AI for CRUD, tests, and refactoring.
❌ Don’t rely on AI for critical system design decisions.

3. Keep Security in Mind

Avoid pasting sensitive code (API keys, proprietary algorithms) into public AI tools.
If security is critical, consider on-device AI tools like Tabnine that don’t send code to external servers.
Always validate AI-generated code against common vulnerabilities (e.g., SQL injection, XSS).

4. Treat AI as a Pair Programmer

Think of AI as your coding buddy, not a replacement. Use it to:

Speed up tasks.
Generate examples you can learn from.
Provide alternative implementations for comparison.

Example workflow:

Write a function.
Ask AI for tests.
Request a refactor suggestion.
Compare and decide what to adopt.

5. Maintain Your Skills

Over-reliance on AI can make you a passive coder. To avoid skill decay:

Study the code AI generates—understand why it works.
Use AI as a learning resource, not just a shortcut.
Continue practicing problem-solving without AI assistance.

6. Collaborate with Your Team

When working in teams:

Agree on when and how to use AI.
Review AI-generated code through pull requests.
Use AI responsibly to avoid inconsistent styles across the codebase.

✅ By following these practices, you can make the most out of AI-powered coding while keeping your codebase secure, maintainable, and professional.

Conclusion + Next Steps

AI is transforming the way developers work. Instead of wasting time on repetitive coding tasks like boilerplate generation, unit tests, refactoring, and documentation, you can now let AI handle the heavy lifting. This frees you up to focus on solving complex problems, building innovative features, and improving user experience.

Throughout this tutorial, we explored how AI can:

Generate boilerplate code in seconds.
Write unit tests that improve code coverage.
Refactor and optimize messy or inefficient code.
Automate documentation for better collaboration.

But remember—AI isn’t a replacement for your skills. It’s a powerful assistant, like a pair programmer, that boosts your productivity when used wisely. Always review AI-generated code, prioritize security, and continue improving your own coding abilities.

Next Steps

Experiment with tools like GitHub Copilot, ChatGPT, or Tabnine in your daily workflow.
Start small—use AI for repetitive tasks, then gradually expand its role in your projects.
Share AI-generated improvements with your team and gather feedback.
Keep learning how AI evolves, as new tools and models are released regularly.

By embracing AI responsibly, you’ll not only write code faster but also become a more efficient and effective developer. 🚀

That is just the basics. If you need more deep learning about the AI, ML, and LLMs, you can take the following cheap course:

Happy Coding!

How to Use AI to Automate Repetitive Coding Tasks

Learn how to use AI to automate repetitive coding tasks, from generating boilerplate code to writing tests and refactoring, and boost productivity.

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