When GitHub Copilot launched, it promised to change the way we write code — forever. A year later, does it live up to the hype? In this post, I break down my personal experiences using Copilot across different workflows: from greenfield development to unit testing and writing documentation.
What Is GitHub Copilot?
GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI. It suggests code completions, snippets, and even entire functions directly in your editor. It works contextually — using your file, your function, and even comments to predict what you’re trying to write next.
Real-World Use Cases
- Boilerplate & Repetitive Code: Great for scaffolding repetitive code like components or test stubs.
- Writing Unit Tests: Type a comment and Copilot often nails the expected logic.
- Regex and YAML: Surprisingly effective at writing configs and regex.
Example: Copilot Writing a Test Suite
describe('add', () => {
it('adds two numbers', () => {
expect(add(2, 3)).toBe(5);
});
it('handles negatives', () => {
expect(add(-1, -2)).toBe(-3);
});
});
Where Copilot Falls Short
- Struggles with new or internal APIs
- May suggest insecure or incorrect logic
- Not consistent with variable naming
Security Considerations
Never blindly accept Copilot suggestions in authentication, encryption, or input validation. Use linters and security scanning tools to catch issues.
Copilot vs. ChatGPT for Devs
| Feature | Copilot | ChatGPT |
|---|---|---|
| Inline Code Suggestions |  |
 |
| Explaining Code | |
|
Tips for Better Copilot Use
- Write comments first to guide suggestions
- Use meaningful variable/function names
- Always review before accepting
Verdict
Copilot is a powerful tool when used properly. Treat it like a junior dev: helpful, but in need of review. Paired with good habits and testing, it’s a productivity boost worth adopting.