How to Use GitHub Copilot to Debug Your Code in 30 Minutes

Debugging can feel like searching for a needle in a haystack. As indie hackers and solo founders, we often juggle multiple roles, and debugging can eat up precious time we could spend building. Enter GitHub Copilot, an AI-powered coding assistant that can help streamline this process. In this guide, I’ll show you how to leverage GitHub Copilot for debugging in just 30 minutes.

Prerequisites: What You Need Before You Start

Before diving in, make sure you have the following:

1. GitHub Account: Sign up for free if you don't have one.
2. Visual Studio Code: Download and install VS Code, as GitHub Copilot integrates seamlessly with it.
3. GitHub Copilot Subscription: As of 2026, Copilot costs $10/month or $100/year, with a free trial available for new users.

Step 1: Install GitHub Copilot

1. Open VS Code: Launch Visual Studio Code.
2. Install the Extension: Go to the Extensions view (Ctrl+Shift+X) and search for "GitHub Copilot." Click install.
3. Sign In: You’ll need to authenticate your GitHub account to enable Copilot.

Expected output: You should see a Copilot icon in your editor, indicating it’s ready to assist.

Step 2: Load Your Code

1. Open Your Project: Navigate to the folder containing the code you want to debug.
2. Select a File: Open a file where you suspect there might be bugs.

Expected output: Your code should be displayed in the editor, ready for analysis.

Step 3: Identify Bugs

1. Commenting for Clarity: Start by adding comments in your code where you think the issues lie. For example, // This function doesn’t return the expected value.
2. Triggering Copilot: Position your cursor at the comment and type // Copilot, what’s wrong here? (You can phrase it however you like).

Expected output: GitHub Copilot will suggest potential reasons for the bug or provide code snippets that could resolve the issue.

Step 4: Review and Implement Suggestions

1. Evaluate Suggestions: Copilot will generate several suggestions. Take a moment to read through them and select the one that seems most relevant.
2. Insert Code: Accept the suggestion by pressing Tab or manually insert it into your code.

Expected output: The proposed fix will replace your comment or be added to your code.

Step 5: Test the Fix

1. Run Your Code: Use your preferred testing method (console logs, unit tests, etc.) to see if the bug is resolved.
2. Iterate if Necessary: If the issue persists, repeat steps 3 and 4, refining your comments and prompts to guide Copilot better.

Expected output: Your code should run without the previous errors.

Troubleshooting: What Could Go Wrong

• Copilot Doesn’t Understand the Context: If the suggestions don’t make sense, try providing more context in your comments.
• Code Still Doesn’t Work: Sometimes, the issue might be deeper than what Copilot can identify. In these cases, consider debugging manually or consulting community forums.

What’s Next: Building on Your Debugging Skills

Once you’ve mastered debugging with GitHub Copilot, consider exploring these next steps:

• Integrate Copilot into Your Daily Workflow: Use it not just for debugging but also for writing new features.
• Explore Other AI Tools: Consider tools like Tabnine or Kite for additional assistance in your coding journey.
• Join the Community: Engage in forums or Discord servers focused on AI coding tools to share experiences and learn tips from other developers.

Conclusion: Start Here for Efficient Debugging

In our experience, GitHub Copilot is a powerful ally in debugging, especially when you’re on a tight deadline. By following the steps outlined above, you can save time and frustration. Remember, it’s not a silver bullet, but with a little patience and practice, it can significantly streamline your debugging process.