How to Use GitHub Copilot for Faster, Smarter Code Reviews

Writing and reviewing code can take a lot of time, especially when you're doing the same thing again and again. That’s where GitHub Copilot helps. 

It’s an AI tool that works like a coding buddy. It suggests code, explains what it does, finds bugs, and even writes tests for you. You don’t have to be an expert to use it. 

In this guide, you’ll learn how to set it up, how much it costs, and how to use GitHub Copilot for coding review and other tasks. If you want to save time and write cleaner code, Copilot is a great place to start.

Ready to code smarter? Join Index.dev and work with global teams using AI tools like GitHub Copilot. High-paying remote jobs, matched to your skills.

What is GitHub Copilot?

GitHub Copilot is an AI-powered coding assistant developed by GitHub in collaboration with OpenAI. It helps developers write code faster by offering real-time suggestions, code completions, and chat-based assistance directly within supported IDEs, such as VS Code, JetBrains, and GitHub.com. 

Copilot can generate entire functions, fix bugs, write tests, and explain code based on context. It supports multiple programming languages and integrates with popular development tools. 

With access to advanced AI models like GPT-4 and Claude, Copilot enhances productivity, reduces repetitive work, and assists with learning new concepts. This makes it a powerful tool for both beginners and experienced developers.

How to set up Github Copilot?

Step 1: Create a GitHub Account

1. Go to https://github.com

2. Click on Sign up

3. Enter:

• Username
• Email address
• Password

4. Verify email and complete the captcha

5. Choose a free plan (you can upgrade later)

You now have a GitHub account.

Step 2: Subscribe to GitHub Copilot

1. Go to https://github.com/features/copilot
2. Click “Start free trial” or “Buy Copilot”
3. Choose Copilot for Individuals
4. Add payment details (you won’t be charged during the 30-day trial)
5. Confirm and activate

You'll now see Copilot options in your GitHub account.

Step 3: Enable Copilot Chat

1. Click your profile icon (top-right corner) and click on Settings

2. On the left menu, scroll to: Code, planning, and automation > Copilot

There you’ll see options to:

• Enable Copilot
• Start your subscription
• Manage billing

3. Ensure the following are enabled:

• GitHub.dev (Web Editor)
• Copilot Chat

This makes Copilot available on the GitHub web interface and the github.dev editor.

Step 4: Open Copilot Chat from the GitHub Dashboard

1. Go to https://github.com/copilot
   (or click the Copilot icon in the top-right of GitHub if available)
2. You'll land on the Copilot Chat dashboard
3. Here, you can:
   • Type a coding task or question
   • Use suggested actions like:
     • "Create an issue for a bug"
     • "Summarize a pull request"
     • "Get code feedback"
     • Attach files (such as code snippets and diagrams) and ask Copilot to interpret them.

GitHub Copilot pricing and plans

Free Plan – $0/month

• Ideal for getting started or casual usage
• 2,000 code completions/month
• 50 chat or agent mode requests/month
• Access to basic models like GPT-4.1, Claude 3.5, Gemini Flash
• Real-time inline suggestions and chat
• Includes debugging help, test generation, and documentation prompts
• Free for verified students, teachers, and open-source maintainers

Pro Plan – $10/month or $100/year

• Most popular plan for active developers
• Unlimited code completions and chats
• Unlimited agent mode with GPT-4.1
• Access to advanced models: Claude 3.7, Gemini 2.5 Pro, OpenAI o3
• Includes code review tools and pull request summaries
• 300 premium requests/month for advanced features
• Works across all supported IDEs and GitHub platforms

Pro+ Plan – $39/month or $390/year

• Best for power users, AI researchers, and enterprise developers
• Includes everything in Pro, plus:
  • Access to top-tier models: GPT-4.5, Claude Opus 4, OpenAI o4-mini
  • 1,500 premium requests/month (30x Free)
  • Early access to features like Copilot Coding Agent (preview)
  • Maximum model flexibility and premium compute usage

Discover the top 5 AI coding agents of 2025.

GitHub Copilot features and capabilities

1. Copilot Chat: Your AI Pair Programmer

Copilot Chat lets you ask coding questions, generate code, and explain logic in plain English, directly inside GitHub or VS Code.

What it can do:

• Explain the code line by line
• Write or refactor functions
• Generate test cases
• Fix bugs and errors
• Guide you through config files, workflows, and more

2. Copilot Spaces: Context That Stays with You

Copilot Spaces is a new smart workspace that allows you to group your code, documentation, and prompt files together. Once you set a context, Copilot understands everything within it across sessions and conversations.

What makes it special:

• Add code files, .md docs, and .prompt.md files
• Create reusable “Spaces” for different tasks
• Ask context-aware questions like:
  • “Generate API tests for this module”
  • “Summarize the logic in this repo”

3. Copilot Coding Agent (Preview)

GitHub is testing a powerful new feature called the Copilot Coding Agent, which can:

• Pick up an issue
• Plan the solution
• Write code and tests
• Submit a pull request
• Respond to feedback and changes

It acts like a virtual developer that works in your team’s background.

4. Prompt Files: Save and Reuse AI Instructions

With .prompt.md files, you can now define reusable prompts and logic instructions for Copilot.

Use cases:

• Test case generation prompt
• Documentation writing prompt
• Code review prompt

Why it's powerful:

• Keeps responses consistent across your team
• Embeds AI-driven standards directly into your workflow

5. Enterprise-Grade Security & Control

Copilot for Business and Enterprise offers:

• No training on your private code
• Admin control over Copilot features
• SOC 2 Type II compliance

Special GitHub Copilot Features Across All Plans

• Copilot Chat and inline support
• Context-aware suggestions
• Integration with GitHub, VS Code, JetBrains, Xcode, etc.
• Prompting via slash commands
• Code explanations, doc/test generation
• Privacy-focused: your code is excluded from training
• Custom instructions and extensions are supported

5 ways to use GitHub Copilot for coding and developer tasks

1. Create a dynamic BMI calculator

Goal: Demonstrate the ability to build a complete, working UI-based project.

We aim to create a dynamic BMI calculator with a similar UX to the one shown in the image below. Github Copilot will require the use of HTML, CSS, and JavaScript code for the task. 

Prompt to use: 

Build a responsive BMI calculator using HTML, CSS, and JavaScript. The layout and styling should closely match the design shown below:

UI Requirements:

• A grey container with a white box at the top for the "Standard" unit.
   
• Input fields:
  • Weight (in pounds)
  • Height (two separate fields: one for feet, one for inches)
     
• A "Your BMI:" result box with a dynamic value.
   
• A colour-coded BMI category bar at the bottom with the following:
  • Underweight: < 18.5 (orange)
  • Normal weight: 18.5 - 25 (green)
  • Overweight: 25 - 30 (orange-red)
  • Obese: > 30 (red)
     

Functional Requirements:

• Calculate BMI using the formula:

BMI = (weight in pounds / (height in inches)^2) × 703

• Dynamically update the BMI value and colour bar category upon input change.
   
• Ensure values like feet, inches, and weight cannot be negative.
   
• Highlight the appropriate BMI category based on the result.
   

Use vanilla HTML/CSS/JavaScript—no frameworks. Structure your code in separate files:

• index.html for layout
   
• style.css for design
   
• script.js for logic

GitHub Copilot response:

From a single prompt, Copilot generated this fully functional BMI calculator with a clean UI, responsive design, real-time validation, and dynamic colour feedback. It handled HTML structure, modern CSS styling, and precise JavaScript logic, proving it can build an end-to-end web app that’s both smart and polished.

2. Add function docstrings

Goal: Clearly describe a function’s purpose, inputs, and outputs using standardised Python docstrings.

We will provide Copilot a function to enhance code clarity, comprehension, and collaboration by generating descriptive docstrings that explain what a function does, its inputs, outputs, and intended behaviour, using natural language prompts in GitHub Copilot.

Prompt: 

"Add a docstring for this function"

def add(a, b):

    return a + b

GitHub Copilot response:

As a developer, I’d say this is a solid, clean response from Copilot. The docstring is concise, follows a standard format, and clearly explains the function's purpose, inputs, and output. It’s beginner-friendly, readable, and production-ready. 

For simple functions like add(), this level of documentation is appropriate and shows how Copilot supports best practices.

3. Refactor the code for clarity

Goal: Improve code efficiency and readability by simplifying or optimising loop logic.

We'll take a basic or repetitive loop and use GitHub Copilot to refactor it into a cleaner, more Pythonic version, utilising techniques such as list comprehensions or built-in functions.

Prompt:

# Refactor this loop for better performance

nums = [1, 2, 3, 4, 5]

squares = []

for i in range(len(nums)):

    squares.append(nums[i] ** 2)

 

print(squares)

GitHub Copilot response:

Copilot’s response is spot-on. It replaced a longer loop with a list comprehension, which is a shorter way to create new lists in Python. This not only makes the code easier to read but also runs a bit faster. 

For larger projects, this type of smart refactoring saves time and keeps the codebase clean and easy to manage.

4. Write unit tests

Goal: Automatically generate unit tests to validate component behaviour and improve test coverage

We'll prompt GitHub Copilot to write a simple React component and ask it to write Jest unit tests. This helps confirm the component renders correctly and responds to different input values or edge cases.

Prompt: 

function Greet({ name }) {

  return <h1>Hello, {name}!</h1>;

}

// Write Jest unit tests for the above React component

GitHub Copilot response: 

Copilot did a great job here. It wrote four clear Jest unit tests that check how the Greet component behaves with different inputs: a name like “Alice,” another like “Bob,” an empty string, and even when no name is given at all. 

This type of testing is extremely helpful in large applications because it catches bugs early and ensures consistent behaviour as the app evolves.

5. Write SQL query prompt using Copilot

Goal: Generate SQL queries from natural language to retrieve, sort, and join data efficiently.

We'll create customers and orders tables, insert sample data, and write an SQL JOIN query. The query will combine order and customer details, sort by order amount in descending order, and display the top 5 results.

Prompt:

Generate SQL code that:

1. Creates a customers table with customer_id and customer_name.
2. Creates an orders table with order_id, customer_id, amount, and order_date, where customer_id is a foreign key referencing customers.
3. Inserts dummy data: 3 customers (Alice, Bob, Charlie) and 6 orders with varying amounts.
4. Performs an INNER JOIN to return the top 5 orders by amount in descending order, including order_id, amount, and customer_name.

GitHub Copilot response:

Copilot did a great job. It created the tables, added sample data, and provided the correct query. This tool is super helpful for big projects because it saves time and does boring, repetitive work fast, like a smart helper who writes code for you. 

5 ways to use GitHub Copilot for your code review process

1. Explain code (line-by-line)

Goal: Break down each line of the code in simple, beginner-friendly language.

In this task, we’ll go through the Python code step by step. You’ll learn what each line does, why it's needed, and how it contributes to calculating the factorial of a number. 

We'll use plain terms to help junior developers follow along confidently.

Prompt:

Explain this code line by line, like I'm a junior developer

def factorial(n):

    result = 1

    for i in range(2, n + 1):

        result *= i

    return result

GitHub Copilot response: 

The response does a great job explaining the code in a clear, beginner-friendly way. Each line is broken down with just enough detail to understand the logic without being overwhelming. 

It aligns well with the goal of helping junior developers grasp how the factorial function works. The tool effectively met our objective of simple, line-by-line code explanation.

2. Optimize this Python loop for performance

Goal: Improve a basic Python loop by finding a faster or cleaner alternative.

We’ll run a simple Python loop that squares numbers. Then, we’ll ask if there’s a better way to write it using Python best practices, such as list comprehensions or built-in functions, and test the improvement on OneCompiler.

Prompt:

Is there a faster or cleaner way to write this loop?

numbers = [1, 2, 3, 4, 5]

squared = []

for i in range(len(numbers)):

    squared.append(numbers[i] * numbers[i])

print(squared)

GitHub Copilot response:

We then executed the code on OneCompiler and found that the code worked accurately.

GitHub’s response is spot on. Replacing the traditional for loop with a list comprehension makes the code more concise and Pythonic. It improves readability and slightly boosts performance by reducing the number of function calls. 

For small-scale operations like this, it’s a clean, efficient refactor that aligns with best coding practices.

3. Suggest edge-case tests for a Python function

Goal: Identify edge-case inputs that could break or mislead a function.

We'll test the function using challenging inputs, such as zero, negative numbers, and unexpected types, to verify its reliability. This helps catch bugs early and improve function robustness.

Prompt:

Suggest edge-case tests for this function

def divide(a, b):

    return a / b

GitHub Copilot response:

GitHub's response fully meets our goal by covering critical edge cases, such as zero division, type errors, and floating-point precision issues. It’s highly useful for developers as it anticipates both common and rare failures. 

This level of test coverage strengthens code reliability and helps catch bugs early, exactly what edge-case testing aims to achieve.

4. Review code for style & best practices

Goal: Improve Python code readability and structure by applying best practices, formatting, and clean coding standards. 

We’ll take a poorly formatted Python function and prompt an AI assistant to refactor it. The goal is to enhance readability, add docstrings, utilise f-strings, and adhere to PEP8 guidelines.

Prompt:

Make this code more readable and follow Python best practices

def greet(n):print("Hello, "+n)

GitHub’s response:

The response effectively meets our goal. It adheres to Python best practices, enhances readability, and conforms to the PEP 8 style guide. 

Using type hints, f-strings, and a proper docstring makes the function cleaner and easier to understand, which is great for maintainability in real-world codebases.

5. Generate clarifying questions & feedback for a pull request

Goal: Generate thoughtful reviewer questions and feedback to understand and improve a pull request.

We’ll review a simulated PR, analyse the code and summary, and ask clarifying questions. Then, we’ll provide constructive feedback on naming, logic, testing, and documentation to enhance code quality.

Prompt:

PR Title: Add user authentication middleware  

Summary: Introduced Express.js middleware to handle JWT-based user authentication for protected routes.

I’m reviewing a pull request titled "Add user authentication middleware".

Please generate questions I can ask the author about the logic, edge cases, and tests.

Also, suggest any improvements to naming, clarity, or documentation.

I’m new to this codebase.

GitHub Copilot response:

This response does a great job. It gives smart questions about how the code works, catches errors, and checks for missing tests. It also suggests better names and ways to make the code safer and easier to understand. 

For someone new to the project, this is super helpful. The tool fully met our goal.

Explore 6 best AI tools for coding documentation.

What I liked about using GitHub Copilot for coding

• Saves time on repetitive tasks: 

Copilot quickly writes boilerplate code, basic functions, and test cases so I can focus on logic and structure.

• Improves code quality: 

It suggests cleaner, more Pythonic (or language-appropriate) alternatives that follow best practices and modern standards.

• Great for learning: 

It explains unfamiliar code, functions, or libraries in plain language, perfect for junior devs or when switching tech stacks.

• Smart context awareness: 

Copilot understands the surrounding code and provides suggestions that actually fit, such as variable names or matching syntax.

• Works across languages: 

Whether I’m writing Python, JavaScript, SQL, or even HTML/CSS, Copilot supports it all and adapts accordingly.

• Boosts productivity during reviews: 

It helps spot bugs, suggests test cases, and even generates feedback for pull requests.

• Feels like pair programming: 

Especially with Copilot Chat, it’s like having a knowledgeable teammate ready to explain, refactor, or debug with you.

The limitations of using GitHub Copilot

• Sometimes generates incorrect or insecure code: 

Copilot can hallucinate logic, miss edge cases, or use outdated methods, so you must review everything carefully.

• Lacks deep understanding of business logic: 

It’s great with syntax and structure, but struggles with domain-specific rules or project context without detailed prompts.

• Over-reliance can hurt learning: 

Beginners may accept suggestions blindly without understanding the underlying logic, which can be risky in the long run.

• Inconsistent quality for complex tasks: 

While it excels at simple functions, its output for complex architectures or multi-file logic is hit-or-miss.

• Limited offline support: 

Copilot requires an internet connection to function, which can be a limitation in secure or restricted environments.

• Doesn’t always follow team conventions:

It may not align with your org’s naming standards, formatting preferences, or codebase patterns unless you enforce it manually.

• Not fully customisable (yet): 

While features like prompt files and context-awareness are improving, fine-tuning responses to team-specific needs is still limited.

FAQs

1. Is GitHub Copilot free? 

Yes, GitHub Copilot offers a free plan with limited usage, including 2,000 code completions and 50 chat requests per month. It’s also free for verified students, teachers, and open-source maintainers. For unlimited access, users can upgrade to the Pro or Pro+ plans.

2. What is GitHub Copilot used for? 

GitHub Copilot helps developers write, review, debug, and document code faster using AI. It suggests code completions, generates full functions, explains logic, writes tests, and even helps with pull request summaries, saving time and boosting productivity across multiple programming languages.

3. Is Copilot better than ChatGPT? 

Copilot is better for real-time coding inside IDEs like VS Code or GitHub. It offers code suggestions, in-editor chat, and deeper integration with your codebase. ChatGPT is better for broad discussions, learning concepts, or exploring solutions outside of a specific coding environment.

4. Can Copilot help find bugs during a code review?

Yes, GitHub Copilot can find bugs during a code review, explain logic errors, and suggest test cases. It also generates clarifying questions to help reviewers understand the intent behind the code, making the review process faster and more thorough.

5. Can GitHub Copilot write full functions or components?

Yes, Copilot can generate complete functions, React components, SQL queries, or even small full-stack modules from a well-crafted prompt. It understands the context and provides structured, often production-ready code for common use cases.

6. Can GitHub Copilot help with coding documentation?

Yes, Copilot can generate docstrings, comment blocks, markdown docs, and even structured documentation from your code. It’s useful for keeping your codebase well-documented and helps teams follow consistent documentation practices effortlessly.

7. How do I enable GitHub Copilot in VS Code?

To enable GitHub Copilot in VS Code, you need to install the GitHub Copilot extension from the VS Code Marketplace. After installation, sign in with your GitHub account and follow the prompts to activate your subscription. You can then access Copilot via inline suggestions and the Copilot Chat sidebar.