How to Create a Function in Python Using AI Tools in 30 Minutes

If you're anything like me, you've probably faced the challenge of wanting to write code efficiently while still grasping the underlying logic of programming. Enter AI coding tools, which promise to make our lives easier but can sometimes feel like more hype than help. In 2026, the landscape for AI-assisted coding has evolved significantly, and I'm here to break down how you can create a function in Python using these tools in just 30 minutes.

Prerequisites: What You Need to Get Started

Before diving in, make sure you have the following:

• Basic understanding of Python: Knowing how to write a simple function will help you grasp the AI's suggestions.
• An AI coding tool: We'll be using tools like GitHub Copilot, Tabnine, or Codeium.
• Access to a code editor: VS Code or PyCharm works great.
• 30 minutes of uninterrupted time: You’ll need this to focus on coding.

Step 1: Choose Your AI Tool

Here are some of the best AI coding tools available in 2026, along with their pricing and features:

| Tool | What It Does | Pricing | Best For | Limitations | Our Take | |---------------|----------------------------------------------|----------------------------|-------------------------------|------------------------------------------|-----------------------------------| | GitHub Copilot| AI pair programmer that suggests code. | $10/mo, free for students | General coding assistance | Limited understanding of complex logic | We use it for quick suggestions. | | Tabnine | AI code completion tool for various languages| Free tier + $12/mo pro | Code completion | Sometimes misses context | We like it for its speed. | | Codeium | AI-powered code suggestions and explanations | Free, $19/mo for pro | Learning and guidance | Can be verbose in explanations | We use it for learning new concepts.| | Replit | Collaborative coding environment with AI | Free, $7/mo for pro | Learning and collaboration | Not as powerful for single-user projects | We don't use it for solo work. | | ChatGPT | Conversational AI for coding help | $20/mo | Debugging and explanations | Limited to text responses | We use it for conceptual clarity. | | Sourcery | AI code improvement suggestions | Free, $15/mo for pro | Code refactoring | Limited to Python | We use it for code quality checks. |

What We Actually Use

For creating functions, we primarily rely on GitHub Copilot for its seamless integration with VS Code and quick suggestions. For debugging, ChatGPT is our go-to.

Step 2: Define Your Function’s Purpose

Before you start coding, decide what your function will do. For example, let’s say you want to create a function that takes a list of numbers and returns their sum.

Step 3: Write a Basic Function with AI Assistance

1. Open your code editor and start a new Python file.

2. Type the function header: def sum_numbers(numbers): and let the AI tool suggest the body of the function.

   • With GitHub Copilot, you might see a suggestion like:

     total = 0
     for number in numbers:
         total += number
     return total
     

3. Accept the suggestion or tweak it according to your needs.

Expected Output

After you run the function with a sample list, like print(sum_numbers([1, 2, 3, 4])), you should see 10.

Step 4: Test Your Function

Make sure to test your function with various inputs, including edge cases like an empty list or negative numbers. This is where AI can help suggest additional test cases.

Troubleshooting Common Issues

• AI suggests incorrect logic: Double-check the function against Python documentation or ask your AI tool for clarification.
• Syntax errors: The AI might suggest code that doesn't run due to syntax issues. Make sure to review the code carefully.

What’s Next?

Now that you’ve created your function, consider expanding its functionality. You could add error handling or support for different data types. Explore more complex AI tools or even build your own custom function using AI-generated snippets.

Conclusion: Start Here

Creating a function in Python using AI tools is not only feasible but can be done in about 30 minutes if you have the right tools and approach. Start with GitHub Copilot for coding assistance and ChatGPT for debugging help. As you get comfortable, experiment with other tools to see what fits your workflow best.