GitHub Copilot is a powerful AI pair programmer, but its effectiveness relies heavily on the quality of your instructions. This is where prompt engineering comes in—the skill of strategically structuring your inputs to get the most precise, relevant, and valuable code suggestions.

Think of it as learning to communicate with your AI colleague. The better the instructions you provide in comments or the Copilot Chat interface, the better the results you’ll get, helping you stay in your development flow and accelerate your work.

When to Use Prompt Engineering

Prompt engineering isn’t just for complex algorithms. It’s a key skill for a variety of everyday developer tasks:

• Generating boilerplate or repetitive code: Quickly scaffold functions, classes, or file structures with specific constraints.
• Writing unit tests and documentation: Prompt Copilot to analyze an existing function and generate comprehensive tests or inline documentation.
• Refactoring legacy code: Ask Copilot to suggest ways to improve performance, readability, or adhere to modern best practices.
• Debugging and explaining code: Use Copilot Chat to understand unfamiliar code blocks or identify potential issues by asking targeted questions.
• Learning a new framework or language: Ask for implementation examples for specific features in a particular technology stack.

How to Use Prompt Engineering: Best Practices

The core principle of effective prompting is clarity and context. Here are the best practices:

1. Be Specific and Clear

Vague prompts lead to vague suggestions. Instead of “Make a search function,” specify the requirements:

• Vague: // Add auth
• Better: // Add JWT authentication using Authorization: Bearer token, verify signature with HS256, and return a 401 JSON response on failure.

Mentioning the target language, framework, and expected output format significantly improves precision.

2. Break Down Complex Tasks

For large tasks, break them into smaller, sequential steps (known as “Chain of Thought” prompting). This helps Copilot build the solution incrementally, leading to a more structured and accurate final result.

For example, when creating a word search generator, you can ask for the functions one by one:

• // 1. Write a function to generate a 10x10 grid of letters.
• // 2. Write a function to find all valid words in the grid given a list of words.
• // 3. Write a function that combines the above to print a grid containing at least 10 words.

3. Provide Relevant Context

Copilot works best when it has local hints.

• Inline suggestions: Add comments just above the code you want to generate.
• Copilot Chat: Highlight relevant lines of code or use the #file or #codebase commands to explicitly reference other parts of your project. This prevents the AI from making brittle assumptions or ignoring existing helpers.

4. Use Examples and Constraints

“Few-shot prompting” (providing a small, correct example) helps Copilot mirror your existing patterns, naming conventions, and error handling styles.

• Mention performance goals (e.g., “O(n log n) or better”) or security constraints (“No eval(): use parameterized queries only”) to guide the AI toward safer, more efficient solutions.
• Define TypeScript interfaces or add type hints in docstrings before the function logic, and Copilot will align its suggestions to that shape.

5. Iterate and Refine

Your first prompt won’t always be perfect. Expect to have a back-and-forth conversation with Copilot Chat. If the suggestion is off, try rephrasing your prompt or adding more detail in a follow-up message:

• “That code uses an external library; rewrite it using only Python’s standard library.”
• “Improve the error handling in the previous function to log the full stack trace.”

By mastering these prompt engineering techniques, you transform GitHub Copilot from a helpful autocomplete tool into a powerful, precise collaborator, maximizing your efficiency and code quality. You can explore further prompt engineering resources on the Microsoft Learn platform.