How to Use GitHub Copilot to Improve Your Code Quality in Just 1 Hour

You’re a solo founder or indie hacker, juggling multiple responsibilities, and the last thing you need is to spend hours debugging your code. Enter GitHub Copilot, an AI-powered coding assistant that promises to enhance your coding efficiency and improve code quality. It’s not magic, but if you know how to leverage it, you can see noticeable improvements in just one hour. Let’s dive into how you can make the most of GitHub Copilot in your coding workflow.

Prerequisites: What You Need Before Getting Started

Before you jump in, here’s what you’ll need:

• A GitHub account: Sign up if you don’t have one.
• Visual Studio Code (VS Code): Download and install it if you haven’t already.
• GitHub Copilot subscription: As of August 2026, it costs $10/month or $100/year, offering a free trial for the first 30 days.
• Basic understanding of Git and coding: Familiarity with JavaScript, Python, or another language will help you get the most out of Copilot.

Step-by-Step Guide to Improve Code Quality

Step 1: Install GitHub Copilot

1. Open VS Code.
2. Navigate to the Extensions view by clicking on the Extensions icon in the Activity Bar.
3. Search for "GitHub Copilot" and click "Install."
4. Once installed, sign in with your GitHub account to activate it.

Step 2: Start Coding with Copilot

1. Open a new or existing file where you want to write code.
2. Begin typing a function or comment describing what you want to achieve. For example, type // function to calculate factorial.
3. Watch as Copilot suggests code snippets. You can accept a suggestion by hitting Tab.

Step 3: Review and Iterate on Suggestions

1. After accepting a suggestion, ensure that you understand the code. Copilot is good, but it’s not perfect.
2. Modify the generated code as needed. This might involve changing variable names or adjusting logic.
3. Test the code frequently. Run unit tests to verify that the output is correct and that the code meets your quality standards.

Step 4: Use Copilot for Code Reviews

1. Copy a block of existing code and paste it into a new file.
2. Write comments about potential improvements, like // optimize this function.
3. Let Copilot suggest refactors or optimizations, then review the output critically.

Step 5: Learn from Copilot’s Suggestions

1. When Copilot generates code, take time to study it.
2. If you’re unfamiliar with a suggested method or library, look it up. This will enhance your coding knowledge and improve your skills over time.

Expected Outputs

By the end of this one-hour session, you should have:

• Improved code snippets that are cleaner and more efficient.
• A better understanding of how Copilot works and how to leverage its suggestions.
• A few new coding techniques or best practices learned from the suggestions provided.

Troubleshooting: What Could Go Wrong

• Inaccurate Suggestions: Sometimes, Copilot may suggest code that doesn’t work as expected. Always validate the output.
• Over-reliance: Don’t rely solely on Copilot. Use it as a tool, not a crutch.
• Limited Language Support: While Copilot supports many languages, it’s still not perfect for every programming language or framework.

What’s Next: Building on Your Copilot Experience

Once you’ve spent an hour with Copilot, here are some next steps:

• Experiment with more complex codebases to see how Copilot adapts.
• Integrate Copilot into your regular coding routine to keep improving your code quality.
• Share your experiences in coding communities to learn from others and refine your approach.

Conclusion: Start Here

GitHub Copilot can significantly enhance your coding quality, but it’s not a silver bullet. Use it wisely, review its suggestions, and continue to learn. If you’re ready to give it a shot, the setup takes about 15 minutes. After that, dedicate an hour to explore its capabilities. You might be surprised by how much it can help you improve your code quality.

What We Actually Use

In our experience at Built This Week, we use GitHub Copilot alongside other tools like ESLint for code linting and Prettier for code formatting. This combination helps us maintain high code quality while leveraging the speed of AI suggestions.