Getting Started with GitHub Copilot: Your AI-Powered Coding Companion
How an AI coding assistant changed the way I write software—and how it might change yours too
My First Week with Copilot
Last month, I was sceptical about AI coding tools. Like many developers, I worried about becoming too dependent on automated suggestions or losing touch with fundamental programming skills. Then I spent a week building a REST API with GitHub Copilot, and my perspective shifted entirely.
The difference wasn't just speed—though I did write code faster. It was the cognitive load. Instead of mentally switching between "what do I want to accomplish?" and "how do I implement this in JavaScript?", I could focus on the former while Copilot handled much of the latter. The result felt less like using a tool and more like having a knowledgeable colleague who never gets tired of writing boilerplate code.
What Sets Copilot Apart
Traditional code completion tools suggest the next few characters or complete a method name. Copilot operates at a higher level, understanding context from comments, function names, and surrounding code to generate entire functions, classes, or even test suites.
When you write a comment like "validate email address and return error if invalid," Copilot doesn't just autocomplete—it generates a complete validation function with proper regex patterns and error handling. It's trained on billions of lines of public code, so it recognizes common patterns and can adapt them to your specific context.
The tool supports over 30 programming languages and integrates directly into your editor, making suggestions feel native to your workflow rather than intrusive.
Getting Started: Installation and Setup
Getting Copilot running takes about five minutes. You'll need a GitHub account with a Copilot subscription (GitHub offers a free trial), and while it works with several editors, Visual Studio Code provides the most seamless experience.
Install the GitHub Copilot extension from the VS Code marketplace, sign in with your GitHub account when prompted, and you're ready to go. The extension includes both inline suggestions and a chat interface for more complex queries.
Here's a quick test: create a new file and type // Create a function that reverses a string. Copilot should suggest a complete implementation. Press Tab to accept it, or use Alt + ] and Alt + [ to cycle through alternative suggestions.
Making Copilot Work for You
The key to effective Copilot usage is learning to communicate your intent clearly. Think of it as pair programming with someone who's excellent at implementation but needs clear direction about what you're trying to accomplish.
Write descriptive comments. Instead of // sort array, try // sort users by last name, then first name, case-insensitive. The more context you provide, the better Copilot's suggestions become.
Use meaningful function and variable names. A function called processUserData will get different suggestions than one called validateRegistration, even with identical comments.
Break complex tasks into steps. Rather than asking for an entire authentication system, start with individual pieces: password validation, token generation, middleware creation. Copilot excels at implementing well-defined, focused functions.
Real-World Example: Building an API Endpoint
Here's how Copilot can help with a typical development task. Let's say you're building a user registration endpoint:
// Create Express endpoint for user registration
// Validate email format and password strength
// Hash password with bcrypt
// Save user to database
// Return success response with user ID
From this comment, Copilot generates a complete endpoint including input validation, error handling, password hashing, and database interaction. You'll still need to review and potentially modify the code—Copilot might assume a different database structure or error handling approach than you prefer—but it provides a solid starting point that handles the common concerns.
Beyond Code Generation
Copilot's chat interface (Ctrl+Shift+I in VS Code) opens up additional possibilities. You can ask it to explain complex code, suggest improvements, or help debug issues. It's particularly useful when working with unfamiliar libraries or trying to understand legacy code.
For example, you might ask "Why is this React component re-rendering unnecessarily?" and get a detailed explanation along with suggestions for optimization. Or request "Refactor this function to use async/await instead of promises" and receive a complete rewrite.
Addressing Common Concerns
"Will this make me a worse programmer?" This concern is understandable but misplaced. Copilot handles routine implementation details, freeing you to focus on architecture, problem-solving, and business logic. It's similar to how calculators didn't make mathematicians worse at math—they just shifted focus to more complex problems.
"What about code quality?" Copilot's suggestions aren't always perfect, but they're generally solid and follow common conventions. You still need to review, test, and refine the code. Think of it as an experienced developer's first draft rather than production-ready code.
"Is my code being used to train the model?" GitHub is transparent about this: your code isn't used to train future versions of Copilot. The tool suggests code based on its training data (public repositories) and your current context, but it doesn't learn from your specific codebase.
Getting the Most Value
Start by using Copilot for routine tasks: writing tests, creating utility functions, implementing common patterns. As you become comfortable with its suggestions, gradually expand to more complex scenarios.
Pay attention to the quality of suggestions and adjust your commenting style accordingly. You'll quickly learn which types of descriptions produce the most useful results.
Most importantly, maintain a critical eye. Copilot is a powerful assistant, but you're still the architect. Review its suggestions, understand what they do, and modify them to fit your specific requirements and coding standards.
The Bigger Picture
GitHub Copilot represents a shift in how we think about coding tools. Rather than replacing human creativity and problem-solving, it augments our capabilities by handling routine implementation work. This allows developers to spend more time on the interesting challenges: designing systems, solving complex problems, and creating great user experiences.
Whether you're a seasoned developer looking to boost productivity or someone newer to programming who wants to focus on learning concepts rather than syntax, Copilot offers a compelling value proposition. The tool isn't perfect, and it won't replace the need for programming knowledge, but it can significantly change how you approach writing code.
Try it for a week on a real project. You might find, as I did, that the question isn't whether AI coding tools are useful—it's how you can best integrate them into your development workflow.
Have you tried GitHub Copilot or other AI coding tools? I'd love to hear about your experiences and how they've impacted your development process.