7 Ways GitHub Copilot Makes Developers More Productive (Not Just AI Coding)

As developers, we're constantly looking for tools to enhance our productivity without sacrificing quality. GitHub Copilot has emerged as one such tool, but there's a significant difference between using AI as a helpful assistant versus relying on it to write entire applications. This distinction is crucial for understanding how to leverage AI effectively in your development workflow.

The Difference Between AI Assistance and AI Replacement

There's an ongoing debate about AI's role in development. Some industry leaders, including Microsoft's CTO and Anthropic's CEO, predict that AI will eventually write the majority of code. While these predictions vary in timeline and scope, they raise important questions about how we should integrate AI into our workflows today.

The reality is that AI can make developers more productive when used correctly. But there's a significant difference between using AI to assist your coding process versus letting it generate entire applications without oversight.

Despite AI advancements, there will continue to be strong demand for skilled developers who know how to effectively leverage these tools

Why GitHub Copilot Is Named "Copilot" (Not "Autopilot")

The name "Copilot" was deliberately chosen—it suggests partnership rather than replacement. Just as an aircraft copilot assists the main pilot without taking full control, GitHub Copilot works best when developers maintain control of the overall coding process while leveraging AI for specific tasks.

This distinction is crucial. When you use Copilot as a true co-pilot—letting it handle routine tasks while you focus on architecture, logic, and quality—you'll likely find yourself more productive than if you tried to have it generate entire applications from scratch.

7 Effective Ways to Use GitHub Copilot

Smart Code Suggestions: Use the tab key to accept intelligent suggestions that appear as you type, significantly speeding up routine coding tasks.
Next Line Suggestions: Leverage Copilot's ability to predict what you're likely to write next based on context.
Making Similar Changes: When you need to make similar (but not identical) changes across your codebase, Copilot can recognize patterns and suggest appropriate modifications.
Parameter Updates: When adding parameters to functions, Copilot can help update all the places where that function is called.
Inline Chat for Specific Code Blocks: Use the inline chat feature to get help with specific code blocks that need adjustment.
Documentation Generation: Have Copilot help generate documentation for your code.
Testing Assistance: Get help creating unit tests based on your implementation code.

Copilot's tab completion feature works best when making similar changes across your codebase

When Copilot Is Most Valuable

GitHub Copilot excels in specific scenarios that every developer encounters regularly. Understanding these use cases helps you leverage the tool most effectively:

When making similar changes across multiple files
When updating function signatures and need to update all calling code
When writing boilerplate code that follows established patterns
When implementing standard algorithms or data structures
When working with unfamiliar APIs or frameworks

The key insight is that Copilot is most helpful when you're making incremental changes to existing code or implementing well-understood patterns—not when trying to generate complex, novel solutions from scratch.

The Limitations of WIP (Write It in Prompt) Coding

While some developers advocate for "WIP coding"—essentially describing what you want in natural language and having AI generate the entire implementation—this approach has significant limitations for professional development:

Generated code often lacks important optimizations
Security vulnerabilities may be introduced without your knowledge
Complex business logic is difficult to capture in prompts
Maintaining and extending AI-generated code becomes challenging
You miss the opportunity to deepen your understanding of the problem domain

This isn't to say that WIP coding has no place—it can be useful for prototyping or generating starter code. But for production applications, it's generally more effective to maintain control of the coding process while using AI for assistance rather than delegation.

While some developers advocate for complete AI-generated code, a balanced approach typically yields better results

Practical Code Example: Leveraging Copilot for Function Updates

Here's a practical example of how Copilot can assist with a common development task—updating a function signature and all its callers:

JAVASCRIPT

// Original function
function calculateTotal(items) {
  return items.reduce((sum, item) => sum + item.price, 0);
}

// Updated function with new parameter
function calculateTotal(items, applyDiscount = false) {
  const subtotal = items.reduce((sum, item) => sum + item.price, 0);
  return applyDiscount ? subtotal * 0.9 : subtotal; // 10% discount when applied
}

When you make this change, Copilot will likely suggest updates to calling code throughout your project:

JAVASCRIPT

// Original caller
const total = calculateTotal(cartItems);

// Copilot might suggest updating to:
const total = calculateTotal(cartItems, shouldApplyDiscount);
// Or even:
const total = calculateTotal(cartItems, user.hasDiscountCode());

This kind of assistance saves time while ensuring consistency across your codebase—precisely the type of task where AI shines without taking over the creative aspects of development.

Finding the Right Balance for Your Workflow

The most effective approach to using GitHub Copilot varies depending on your experience level, the complexity of your project, and your personal preferences. However, most developers find the greatest productivity gains when they:

Maintain control over architecture and design decisions
Use Copilot for implementation details and repetitive tasks
Verify and understand all AI-generated code before committing
Combine Copilot suggestions with their own expertise
Use Copilot to learn new patterns and approaches

The future of development isn't about AI replacing developers—it's about developers who effectively use AI outperforming those who don't. By understanding GitHub Copilot's strengths and limitations, you can significantly enhance your productivity while continuing to grow your skills as a developer.

Conclusion: Embrace AI as a Partner, Not a Replacement

GitHub Copilot and similar AI tools represent a significant advancement in development productivity. However, their greatest value comes from complementing human expertise rather than replacing it. By using these tools as true co-pilots—assisting with implementation details while you focus on the bigger picture—you can achieve the optimal balance of efficiency and quality.

As AI continues to evolve, the developers who thrive will be those who learn to effectively collaborate with these tools rather than either rejecting them entirely or becoming overly dependent on them. The name "Copilot" serves as an important reminder of this relationship—it's there to assist you on your journey, not to fly the plane by itself.

7 Effective Ways GitHub Copilot Makes Developers More Productive Beyond AI Coding