How to Use GitHub Copilot for Faster Development

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GitHub Copilot has taken AI-powered coding to a new level, but developers may still have questions about how to use GitHub Copilot for faster development. Can it handle complex coding tasks? What makes it better than standard autocomplete tools?

 

Like any emerging technology, there is a learning curve—but helping your developers learn how to use GitHub Copilot effectively can transform the way they work.

In this GitHub Copilot guide, we’ll go into:

• How GitHub Copilot accelerates development
• What are some of the core features of GitHub Copilot
• How your developers can use GitHub Copilot effectively
• Best practices in prompting for success

How GitHub Copilot Accelerates Development

GitHub Copilot takes the grind out of coding, helping developers move from idea to execution in record time.

Cleaner Code, Fewer Errors

GitHub Copilot suggests best practices in real-time, reducing inconsistencies and improving code quality across teams. With standardized patterns, collaboration becomes smoother, and reviewing code takes less time—keeping projects on track.

Smarter, Less Stressful Development

By handling routine coding tasks, GitHub Copilot frees up developers’ mental bandwidth. Developers spend countless hours scouring external documentation and forums for answers. GitHub Copilot delivers those answers directly to your IDE.

One GitHub Copilot tip? It also acts as an on-demand mentor, recommending efficient approaches that help developers of all skill levels refine their skills.

Security-First Coding

GitHub Copilot also supports security with the latest best practices and up-to-date documentation. With intelligent recommendations based on current security standards, your developers can write safer code without sacrificing speed.

Understand Your Codebase Faster

New project commencing? Have new developers onboarding into your codebase? Need to understand legacy code systems? GitHub Copilot helps users quickly grasp existing code by suggesting relevant documentation and guiding them through unfamiliar sections. It connects the dots between what’s written and what needs to be built.

 

 

What GitHub Copilot Can Do

By predicting, generating, and refining code in real-time, GitHub Copilot helps you keep projects moving forward.

Auto-Generated Code, On Demand

Instead of your developers writing repetitive boilerplate from scratch, GitHub Copilot delivers ready-to-use snippets for multiple programming languages. It understands syntax, structure, and best practices, helping your developers stay focused on more valuable work. This is all delivered in-line in your IDE or part of the GitHub Copilot extensions.

Context-Aware Suggestions

GitHub Copilot suggests code edits based on what it has learned from your existing repository files. It recognizes patterns, languages, logic, and variables in your repositories, ensuring that every recommendation aligns with your team’s style and existing practices while providing elevated suggestions when possible.

Debugging Made Easy

Spotting and fixing bugs can slow down development—and GitHub Copilot helps with intelligent debugging assistance. It identifies potential issues, suggests fixes, and refines the developer approach.

GitHub Copilot will actively highlight potential errors and problems it sees within your code. Simply highlight the problem and ask, “How can I fix this issue?” GitHub Copilot will provide relevant documentation or edits to stop those bugs before they become a work item in your system!

Smarter Documentation

Good code needs great documentation. GitHub Copilot helps by generating function descriptions, inline comments, and summaries, making sure your codebase stays readable and maintainable without extra effort. It generates these based on its context-aware base as well, so you can be reassured the documentation GitHub Copilot is providing relates to your code base.

Multi-Line Completions for Faster Workflows

GitHub Copilot predicts entire functions. With intelligent, multi-line completions, your developers spend less time writing boilerplate and more time shipping code.

What to Keep in Mind for Tiers of Service

Depending on what you need for scale of service, there are several different tiers available:

• Free provides individual developers with limited access to GitHub Copilot’s features. This is ideal for those who want to test Copilot before committing to a paid plan.
• Pro unlocks full access to most features, including advanced AI-powered completions and unlimited usage. This plan is perfect for freelancers, solo developers, and engineers.
• Business allows organizations to manage AI coding policies while providing full Copilot access to employees.
• Enterprise: includes all Copilot Business features with additional AI capabilities tailored for large organizations. Enterprises can manage usage across multiple teams and choose between Business or Enterprise licenses for different groups within their organization.

How to Use GitHub Copilot Effectively for Developers

To get the most out of it, developers need to structure their workflows effectively and use Copilot strategically.

1. Write Better Prompts for Smarter Suggestions

The first of many GitHub Copilot tips and tricks. GitHub Copilot responds best to clear, specific instructions. The more context you provide, the more accurate and useful its suggestions will be.

• Add detailed comments before code blocks to guide responses.
• Specify variable names and programming languages for precise recommendations.
• Avoid vague prompts—refine your inputs iteratively for better results.
• Utilize GitHub Copilot Extensions to get targeted results on topics.

2. Generate Code Faster with Smart Snippets

Start writing a comment or a partial function, and GitHub Copilot fills in the gaps with context-aware recommendations.

For example, to calculate the average of an array in JavaScript, simply type:

// Function to calculate the average of an array

GitHub will generate the rest. This can be done either in-line or with the “edit” function of the IDE extension.

3. Optimize Workflows with Context-Aware Suggestions

GitHub Copilot also understands your project’s structure and suggests the next logical step.

• Writing a conditional statement? GitHub Copilot predicts the most relevant conditions.
• Building a database query? It suggests optimized query structures based on existing patterns.
• Need UI updates? It offers functional adjustments aligned with your codebase.

4. Improve Code Quality with AI-powered Refactoring

Outdated or inefficient code can slow down development. GitHub Copilot suggests refactored alternatives including opportunities to:

• Optimize code (to get it to run more efficiently, for example)
• Clean up repeated code (which is easier to revise)
• Divvy up complicated units (which primes these small units for reuse later)

5. Debug Smarter, Fix Issues Faster

GitHub Copilot highlights common errors and provides real-time debugging assistance. Whether it’s a syntax mistake in Terraform or a missing parameter in a function, it spots the issue and suggests solutions.

6. Use Copilot Agents for Higher-Level Automation

GitHub Copilot agents take AI-assisted coding to the next level by handling structural changes, workflow adjustments, and UI updates behind the scenes.

Integrated with Copilot Chat, these agents can:

• Query documentation and retrieve relevant information.
• Execute pre-defined coding actions and optimize repetitive workflows.
• Assist with testing and backend updates, reducing manual intervention.

7. Set Strong Coding Standards for a More Predictable Experience

For teams working in new environments, structuring projects correctly from the start makes GitHub Copilot’s suggestions more useful.

Define coding patterns, maintain consistent documentation, and set clear standards—GitHub Copilot will learn and follow them, making collaboration smoother and development more efficient.

GitHub Copilot Best Practices for Success

GitHub Copilot is a powerful tool, but using it effectively requires strategy. Developers must balance AI assistance with human expertise to maintain quality, consistency, and security in their workflows.

1. Maintain Code Review Standards

GitHub Copilot generates code quickly, but not all suggestions are perfect. Always review AI-generated code to make sure it aligns with your project’s standards and security requirements.

Developers should:

• Refine prompts to improve output accuracy
• Validate code during peer reviews
• Adjust suggestions to fit existing conventions

2. Customize Copilot to Fit Developer Style

GitHub Copilot learns from patterns, but it won’t automatically match your preferred coding style or patterns. Adjust suggestions to maintain consistency across your codebase, and provide regular feedback to fine-tune its recommendations.

Establish clear team guidelines to ensure Copilot aligns with your programming conventions.

3. Train Copilot for Niche Use Cases

The more domain-specific data GitHub Copilot processes, the better its suggestions become. Developers working in specialized environments—like Azure-native applications—can improve GitHub Copilot’s relevance by using structured prompt templates and sharing knowledge bases.

The goal is to align GitHub Copilot with your team’s development priorities while avoiding generic, one-size-fits-all outputs.

4. Balance Automation with Hands-On Coding

GitHub Copilot makes coding faster, but developers should remain actively involved in structuring applications and making architectural decisions.

Use GitHub Copilot for repetitive or boilerplate tasks while maintaining hands-on problem-solving for complex development work. Over-reliance on automation can lead to gaps in understanding, so developers should continue to sharpen their skills.

5. Leverage Copilot for Azure-Native Development

GitHub Copilot can simplify Infrastructure as Code (IaC) for scalable Azure workloads, assisting with Bicep, Terraform, and ARM templates. It also helps validate PowerShell and Bash scripts for Azure DevOps CI/CD pipelines, maintaining efficient automation while reducing syntax errors.

Yet, as we’ve already discussed, GitHub Copilot does require human oversight when handling architectural or structural decisions. Developers need to provide clear direction for the best results.

 

 

6. Use Agents to Complete Tasks Not Otherwise Specified

GitHub Copilot’s Agent Mode allows developers to work faster with self-healing code, runtime analysis, and iterative problem-solving.

• Automated code iteration: Agents can analyze and improve a solution in real time. When errors arise, GitHub Copilot detects them, makes necessary adjustments, and re-runs the code until the task is fully resolved.
• Smarter terminal commands: In Agent Mode, GitHub Copilot suggests optimized terminal commands and prompts you to execute them.
• Self-healing code: Instead of manually debugging outputs, developers can rely on GitHub Copilot to recognize errors, correct them, and refine execution on its own.

 

GitHub Copilot is no longer just an assistant—it’s an active problem-solver that helps developers code smarter, faster, and with fewer interruptions.