At Zarego, we continually explore tools that can make software development processes better and more productive. Recently, we focused on GitHub Copilot, an AI-powered tool designed to provide real-time code suggestions based on the context of your project. While Copilot promises to enhance productivity, its real value goes beyond what GitHub advertises. This article breaks down key concepts and practical applications to help developers maximize Copilot’s potential.

What Is GitHub Copilot?

GitHub Copilot is an AI-driven tool that assists developers by suggesting code based on the context of their work. It operates as a probabilistic model, meaning it doesn’t always produce perfect results. The developer plays a crucial role in guiding Copilot to make more accurate suggestions by providing the right context and crafting specific prompts.

Key Concepts: Context and Prompt Crafting

To fully harness the power of GitHub Copilot, it’s important to understand two core principles:

• Context: Copilot’s suggestions are heavily influenced by the surrounding code. It analyzes the project environment to determine the most relevant recommendations. The more comprehensive the context, the better the suggestions.
• Prompt Crafting: Writing clear and specific prompts can significantly enhance the quality of Copilot’s suggestions. Precise prompts, such as asking for a form using a specific UI library, will result in more tailored code recommendations compared to vague commands.

Comment-Driven Development

One effective strategy for using GitHub Copilot is comment-driven development. In this approach, the developer writes detailed comments before coding. These comments guide Copilot, providing it with a clear roadmap to follow. By breaking down tasks step-by-step in the comments, you can ensure that Copilot generates more accurate and relevant code.

For example, when building a Next.js form page, starting with a comment that outlines the page structure and required components will lead to better suggestions from Copilot. This method encourages thoughtful planning and helps avoid coding errors down the line.

Developing a Next.js Page with Copilot

An illustrative example of Copilot’s power is the development of a Next.js page with a form. By providing Copilot with prompts that describe the specific requirements—such as using Material UI 5 for styling—developers can quickly generate code that meets the project’s needs. As the complexity of the form increases, adjusting the prompts to include specific form-handling libraries like Formik helps Copilot generate optimal suggestions.

This example highlights how prompt crafting and context directly affect the quality of Copilot’s code output, showing that thoughtful guidance can dramatically improve the results.

Accelerating Code Generation with Copilot

One of Copilot’s standout features is its ability to generate code quickly and efficiently. It’s particularly useful when working on repetitive tasks or generating boilerplate code. By providing a prompt that describes the required functionality, Copilot can generate entire code blocks in seconds, significantly reducing the time spent on manual coding.

For instance, Copilot can quickly draft a deployment script, complete with validations, by analyzing the context and prompts. This feature not only speeds up development but also helps maintain consistency across your codebase.

Optimizing Copilot with Project Context

To get the best out of Copilot, it’s essential to configure its working environment correctly. Including a detailed README file and keeping relevant files open, such as class definitions or key modules, can help Copilot understand the project context more effectively. This enables it to provide more relevant suggestions and minimizes the likelihood of errors or irrelevant code snippets.

Refactoring with Copilot

Copilot isn’t just for generating new code; it’s also a powerful tool for refactoring. It can suggest optimizations and alternative approaches to existing code. By asking Copilot to refactor specific sections, such as extracting types or creating reusable constants, developers can streamline complex codebases and enhance maintainability.

When interacting with Copilot for refactoring, developers can review multiple suggestions and refine the prompts to achieve the desired result. This iterative process ensures that the refactor fits seamlessly within the project’s architecture.

Debugging TypeScript Errors with Copilot

Another valuable feature of Copilot is its ability to explain and debug TypeScript errors. When an error arises, Copilot can provide detailed explanations that help pinpoint the root cause. With the right context, Copilot can simplify the debugging process, making it easier for developers to resolve common TypeScript issues and improve code quality.

By analyzing the error context, Copilot helps developers not only fix the issue but also understand the underlying problem, leading to more robust solutions.

Conclusion

GitHub Copilot is a powerful tool for developers looking to streamline their workflows and boost productivity. Whether you’re generating new features, refactoring existing code, or debugging issues, understanding key concepts like context and prompt crafting can help you get the most out of this AI-powered assistant. Copilot’s ability to assist with everything from basic code generation to complex refactoring makes it an invaluable tool in any developer’s toolkit. At Zarego, we’re excited to continue exploring how AI can enhance the development process, helping our team and our clients achieve better results faster.

This article is based on one of our training sessions. Watch it here (in spanish).