GH-300 Sample Questions Answers

By asking for multiple suggestions using comments in your code

By opening the completions panel in your editor

By using the inline chat functionality with the command ' multiple '

By using @workspace in the chat window

Add teams to the Copilot subscription for an organization

Upgrade or downgrade the subscription tier

Migrate Copilot seat assignments between GitHub organizations

Reassign Copilot seats based on GitHub repository size

Remove teams from the Copilot subscription for an organization

Generate suggestions

Capturing the user ' s context

Processing telemetry data

Retraining the model

Storing user data

Constructs detailed test documentation.

Creates unit tests for the selected code.

Integrates with external testing frameworks.

Executes test cases to find issues with the code.

Code suggestions are guaranteed to not expose known security vulnerabilities

You can use keyboard shortcuts to accept the next word in a suggestion

Code suggestions are limited to single-line suggestions

Code suggestions will always compile or run without modifications

Alternative code suggestions can be shown in a new tab

GitHub Copilot Individual

GitHub Copilot Business

GitHub Copilot Enterprise

GitHub Copilot Professional

Fairness

Transparency

Inclusiveness

Accountability

GitHub Copilot Business for non-GHE customers

GitHub Copilot Individual

GitHub Copilot Enterprise

GitHub Copilot Teams

Inline suggestions

Pull request summaries

Knowledge bases

Chat

Keep the prompt as short as possible, using single words or brief phrases.

Write the prompt in natural language without any programming language.

Avoid mentioning the programming language to allow for more flexible suggestions.

Provide examples of expected input and output within the prompt.

GitHub Copilot can fix syntax errors without user input.

GitHub Copilot can automatically rewrite code to follow best practices.

GitHub Copilot can suggest refactoring improvements for better code quality.

GitHub Copilot can remove unnecessary files from the project directory.

Describing in your prompt what your role is to get a better suggestion

Tell GitHub Copilot in what tone of voice it should respond

Prompt GitHub Copilot to explain what was the role of a suggestion

Giving GitHub Copilot multiple examples of the form of the data you want to use

Initiates a code review with static analysis tools for security and logic errors.

Converts pseudocode into executable code, optimizing for readability and maintainability.

Generates new code snippets based on language syntax and best practices.

Proposes changes for detected issues, suggesting corrections for syntax errors and programming mistakes.

Avoid using Copilot for complex tasks.

Disable Copilot ' s inline suggestions.

Regularly review and refactor the code suggested by Copilot.

Rely solely on Copilot ' s suggestions without reviewing them.

It filters out irrelevant information from the user ' s input prompt.

It enriches the input prompt with additional context before passing it to the language model.

It directly generates a response based on the user ' s input prompt.

It formats the output response before presenting it to the user.

/init-tests

/create-tests

/generate-tests

/tests

Write code comments - > Press the suggestion shortcut - > Select the best suggestion from the list.

Use copilot suggest - > Write the command you want - > Select the best suggestion from the list.

Type out the code snippet - > Use the copilot refine command to enhance it - > Review the suggested command.

Describe the project ' s architecture - > Use the copilot generate command - > Accept the generated suggestion.

The explanations are dynamically updated based on user feedback.

Reviewing and validating the generated output for accuracy and completeness.

GitHub Copilot Chat uses a static database for generating explanations.

The explanations are primarily derived from user-provided documentation.

The excluded content is not directly available to GitHub Copilot to use as context.

GitHub Copilot suggestions are no longer available in the excluded files.

The excluded content is no longer used while debugging the code.

The IDE will not count coding suggestions in the excluded content.

By using chat history to offer personalized code snippets based on previous prompts.

By logging chat history to monitor user activity and ensure compliance with coding standards.

By analyzing past chat interactions to identify common programming patterns and errors.

By sharing chat history with third-party services to improve integration and functionality.

Restricts Copilot to use only external databases for generating code suggestions.

Allows Copilot to generate suggestions based only on the prefix of the code.

Ignores both the prefix and suffix of the code, focusing only on user comments for context.

Improves suggestions by considering both the prefix and suffix of the code, filling in the middle part more accurately.

Apply policies through the GitHub Actions configuration

Create a copilot.policy file in each repository

Configure the policies in the organization settings

Create a copilot.policy in the .github repository

Prompt data is stored unencrypted for faster processing.

Prompt data is used internally by GitHub for improving the search engine.

Prompt data is used to train machine learning models for better code suggestions.

Real-time user input helps generate context-aware code suggestions.

By enforcing strict coding standards that prevent the introduction of vulnerabilities.

By providing detailed reports on the security vulnerabilities present in the codebase.

By annotating the given suggestions with known vulnerability patterns.

By automatically refactoring the entire codebase to remove vulnerabilities.

Modify the .gitignore file to include the specific files.

Add the specific files to a copilot.ignore file.

Use the GitHub Copilot settings in the user interface to exclude files.

Rename the files to include the suffix _no_copilot.

A project plan for the team needs to be generated using a project management software.

Create a documentation file for the newly created code base.

A developer is added to a new project and would like to understand the current software code.

Fast tracking of release management activities to move code to production main branch.

By engaging in real-time collaboration with multiple developers to write boilerplate code.

By predicting future coding requirements and pre-emptively generating boilerplate code.

By refactoring the entire codebase to eliminate boilerplate code without developer input.

By suggesting code snippets that can be reused across different parts of the project.

Chat history is used to train the GitHub Copilot model in real-time.

Chat history provides context to GitHub Copilot, improving the relevance and accuracy of its code suggestions.

Chat history is stored and shared with other users to enhance collaboration.

Chat history is irrelevant to GitHub Copilot and does not affect its functionality.

Enterprise settings → Copilot → Policies

Repository settings → Copilot → Policies

Organization settings → Copilot → Policies

User settings → Copilot → Policies

Blocking public code matches

Blocking license check configuration

Allowing public code matches

Allowing license check configuration

To compare user-generated code against a private repository for potential matches.

To allow administrators to control which suggestions are visible to developers based on custom criteria.

To encourage the user to follow coding best practices preventing code duplication.

To detect and block suggestions that match public code snippets on GitHub if they contain about 150 characters.

It automatically merges pull requests after an automated review.

It generates a prose summary and a bulleted list of key changes for pull requests.

It can validate the accuracy of the changes in the pull request.

It can answer questions about the changeset of the pull request.