7 Prompts for GitHub Copilot: From Commits to Code Review
GitHub Copilot has evolved far beyond a simple autocomplete tool. By mid-2026, it's an integral part of many developers' workflows—helping with everything from writing code to reviewing pull requests. But the key to unlocking its full potential lies in crafting the right prompts. In this article, we'll explore seven specific prompts that will transform how you use Copilot for commits, documentation, and code reviews.
Why Prompts Matter for Copilot
Copilot's underlying model responds to the context you provide. A vague comment like "fix this function" yields a generic suggestion. A precise prompt, on the other hand, can generate production-ready code or a thorough review. According to GitHub's official documentation on Copilot prompt engineering, the more specific you are about the task, language, expected output, and constraints, the better the results.
1. The Commit Message Generator
Prompt:
# Generate a concise git commit message for the following diff:
# [paste diff here]
# Follow conventional commits format: type(scope): description
# Type can be feat, fix, refactor, docs, test, or chore
Why it works: It gives Copilot a clear structure and example. The model will parse the diff and produce a message that fits your team's standards.
Example:
For a diff that adds a login endpoint with JWT authentication, Copilot might generate:
feat(auth): add login endpoint with JWT token generation
2. Code Explainer for Onboarding
Prompt:
# Explain the following code to a junior developer.
# Include: what the function does, its parameters, edge cases, and any potential performance issues.
# Keep it under 10 sentences.
# Code:
[paste code block]
Why it works: It sets the audience level, length, and specific topics to cover. This is invaluable when you're onboarding new team members or reviewing legacy code.
Example:
You paste a complex recursive function for tree traversal. Copilot returns a clear explanation, mentions recursion depth limits, and suggests an iterative alternative for large trees.
3. Code Review Checklist Generator
Prompt:
# You are a senior developer reviewing a pull request for a Python web API.
# List 5 specific things you would check in the code below.
# Focus on: security (SQL injection, input validation), performance (N+1 queries), and code style (PEP 8).
# Code:
[paste code block]
Why it works: It forces Copilot to act as a reviewer with a defined role and focus areas. The result is a structured checklist you can use or adapt.
Example:
Copilot might flag missing input sanitization in a SQL query, suggest adding an index for a frequently accessed column, and recommend renaming a variable to follow PEP 8.
4. Refactoring Suggestion Prompter
Prompt:
# Suggest a refactoring for the following function to improve readability and maintainability.
# Apply the Single Responsibility Principle.
# Return the refactored code and a brief explanation of changes.
# Original code:
[paste code block]
Why it works: It gives a clear principle to apply and asks for both code and explanation. This helps you understand why the change is an improvement.
Example:
A function that both validates email and sends a notification gets split into two functions: validate_email and send_notification. Copilot explains that this separation makes the code easier to test and reuse.
5. Test Case Generator
Prompt:
# Generate unit tests for the following function using pytest.
# Include: normal cases, edge cases (empty input, None, large numbers), and error cases.
# Use descriptive test names.
# Function:
[paste function]
Why it works: It specifies the testing framework, types of test cases, and naming convention. Copilot will produce a comprehensive test file.
Example:
For a function that calculates the factorial of a number, Copilot generates tests for 0, 1, negative numbers, and a large input, each with a clear name like test_factorial_of_negative_raises_error.
6. Documentation from Code
Prompt:
# Write a docstring for the following function in Google style.
# Include: summary, Args, Returns, Raises, and a usage example.
# Function:
[paste function]
Why it works: It defines the exact format and sections. Copilot will produce a docstring that's ready to paste into your codebase.
Example:
For a function that fetches user data from an API, Copilot generates a docstring with parameter descriptions, return type, possible HTTP errors, and requests.get example.
7. Architecture Decision Log (ADR) Generator
Prompt:
# Draft an Architecture Decision Record (ADR) for choosing PostgreSQL over MongoDB for a new project.
# Sections: Title, Context, Decision, Consequences.
# Keep it under 200 words.
Why it works: It gives a clear template and constraints. Copilot can generate a structured ADR that you can refine.
Example:
Copilot produces an ADR explaining that PostgreSQL was chosen due to strong ACID compliance, mature tooling, and team expertise, while acknowledging the trade-off of less flexible schema for early-stage prototyping.
Real-World Application: A Case Study
Consider a mid-sized SaaS company that adopted these prompts for their backend team. The team was spending an average of 30 minutes per developer each day writing commit messages and reviewing pull requests. After training on these seven prompts, they reduced that time by 40%—down to 18 minutes per developer per day. Commit messages followed conventional commits consistently, and code reviews became more thorough, catching security issues earlier.
One developer noted: "The explainer prompt is a lifesaver for onboarding. New hires can paste any function and get a clear explanation in seconds."
Conclusion
GitHub Copilot is only as effective as the prompts you feed it. By using structured prompts for commits, explanations, code reviews, refactoring, tests, documentation, and decisions, you can turn Copilot into a true development partner. Start with the seven prompts above, adapt them to your language and framework, and watch your productivity—and code quality—improve.
For developers using Copilot with multiple services, ASI Biont supports connecting GitHub Copilot and other tools via API—see more at asibiont.com/courses.
Comments