GitHub Copilot is a powerful tool that has been integrated into many IDEs, with the most notable integration currently found in VSCode. The biggest impact lies in its productivity features. In many applications, you can chat with an AI about your code, and it can generate code, documentation, unit tests, and comments—often within a chat window. However, VSCode’s “edit mode” significantly boosts performance by directly modifying files and automating user interactions such as creating folders, files, and updating configuration.
From my experience, I’ve saved a great deal of time by delegating boilerplate tasks to the AI and having it comment code or generate tests. While the generative process is quite good, it depends heavily on how you use it. Many developers claim the AI lacks capability, yet they aren’t taking full advantage of what it can do. Through observation, I’ve noticed that more experienced developers get better results from the AI than those who are less skilled.
This highlights that experience matters. The more technically capable you are, the better you can guide the AI. For instance, AI might not automatically apply patterns and best practices to their fullest extent, but it does understand them. If you provide specific details—such as which pattern you want—it can produce better results.
When it comes to working with Copilot, I see myself as the mind and it as the assistant. If the human directing the AI is unsure of what they want, the AI can help only to a certain degree. Clarity in your goals and in how you plan to achieve them is crucial; you are the orchestrator, and the AI is the productivity tool.
That said, you must decide where AI assistance is genuinely helpful and where you can work faster on your own. Experimentation helps you find that balance.
It’s also essential to keep investing in your own skills. Struggling with complex code challenges is part of learning. The more you know, the more effectively you can leverage the AI by exercising good judgment. For example, when I asked Copilot to review code related to list virtualization, its suggestions were based on common patterns but weren’t necessarily the most efficient. AI often overcomplicates solutions. A good developer tends toward simpler, more efficient code—an area where clear patterns come into play.
Ultimately, the developer must remain the source of expertise. If you keep improving your understanding and let the AI serve as an assistant rather than the one in charge, you can significantly boost productivity. However, relying on the AI to direct everything can quickly lead to complications.