GitHub Copilot Prompt Engineering From Beginner to Expert in 2025

It's been more than four years since I started using GitHub Copilot.

I've used it nearly every day since it first appeared as a simple autocomplete tool. Over time, I've picked up many small lessons that help Copilot perform better.

In this post, I'll share what I've learned — from basic usage to advanced prompting techniques — so you can use Copilot more effectively.

What Is GitHub Copilot?

GitHub Copilot (GHCP) is an AI-powered coding assistant built by GitHub and OpenAI.

Think of it as a coding partner who has read millions of repositories and can suggest what to write next — whether it’s a single line or an entire module.

How GitHub Copilot Works

Copilot predicts your next code based on context. It considers:

• The code around your cursor
• Comments, function names, variable names
• Other open files
• Your full project structure

It behaves like predictive text, but for code.

You mainly use it in two ways:

Inline Suggestions

Appear as you type.
Best for completing small logic blocks, boilerplate, or finishing functions.

Copilot Chat

Used for asking questions, refactoring, or generating tests.
Best when you want control, explanation, or iteration.

How Prompting Actually Works

Under the hood, Copilot follows a simple pipeline:

1. Collects context from your editor
2. Filters relevant sections
3. Feeds them into the model
4. Returns the best predicted suggestion

Basic Prompting Tips

1. Be Clear About What You Want

Good Example ✅

/* Create a user authentication system with:
 * - Email/password login
 * - JWT token generation
 * - Password hashing with bcrypt
 * - Input validation and error handling
 */

Poor Example ❌

// Make login work

2. Give Context Before You Code

Good Example ✅

/**
 * Product API
 * Handles CRUD operations for products using Express and MongoDB.
 * Includes validation, pagination, and image upload.
 */

3. Break Large Tasks into Steps

Better Approach

# Step 1: Validate email
# Step 2: Hash password
# Step 3: Create user record

4. Give Examples

Good Example ✅

// Input: 1234.56, "USD"
// Output: "$1,234.56"
function formatCurrency(amount, currency) {
  // ...
}

5. Use Proper Naming and Comments

Good Example ✅

def calculate_monthly_payment(loan_amount, annual_rate, years):
    """Calculate monthly payment for a loan."""

Helpful Prompting Do’s and Don’ts

Even small prompt changes can alter Copilot’s behavior. Use this table as a quick reference for daily use.

Example Comparison

Bad Prompt ❌

# Make it work

Good Prompt ✅

# Create a CLI tool to track daily expenses
# Features:
# 1. Add and remove entries
# 2. Show total spent
# 3. Save data to a local JSON file

Quick Mental Model

Common Mistakes to Avoid

Pitfall 1: Vague or Ambiguous Prompts

Poor Example ❌

# Fix this function
def process_data(data):
    pass

Better Example

# Fix the user authentication function to:
# 1. Validate email format
# 2. Check password strength
# 3. Hash password using bcrypt
# 4. Handle database errors
def authenticate_user(email: str, password: str) -> Dict[str, Any]:
    pass

Pitfall 2: Inconsistent Code Style

Poor Example ❌

function getUserData() {} // camelCase
function get_user_profile() {} // snake_case
const USERAPI = ""; // inconsistent

Better Example

const API_BASE_URL = "https://api.example.com"; // SCREAMING_SNAKE_CASE
function getUserData() {} // camelCase
function getUserProfile() {} // consistent naming

Pitfall 3: Too Many Open Tabs

Pitfall 4: Missing Examples

Poor Example ❌

# Create a data validation function

Better Example

# Create a data validation function that:
# Input: {"name": "John", "age": 25, "email": "john@test.com"}
# Output: {"valid": True, "errors": []}
def validate_user_data(data: dict) -> dict:
    pass

Final Thoughts

Copilot doesn’t replace developers — it amplifies clarity and speed.

Write comments that describe the goal, not how to achieve it. Give Copilot structure, examples, and constraints. Iterate until the suggestion aligns with your expectation.