Copilot Free in Visual Studio

• Use slash commands to quickly specify common tasks like /explain to get code explanations.
• Scope the chat to specific files using references.
• Review the source used by Copilot to generate the answer.
• Use different threads for each of your Copilot chats so you can maintain different contexts in each.

Learn more about how to use Copilot Chat in Visual Studio.

You can also keep track of conversations surrounding GitHub Copilot through GitHub’s discussion forum.

Learn more about billing for GitHub Copilot.

GitHub Copilot transforms the developer experience. Backed by the leaders in AI, Copilot provides contextualized assistance throughout the software development lifecycle, from code completions and chat assistance in the IDE to code explanations and answers to docs in GitHub and more. With Copilot elevating their workflow, developers can focus on more: value, innovation, and happiness.

GitHub Copilot enables developers to focus more energy on problem solving and collaboration and spend less effort on the mundane and boilerplate. That’s why developers who use Copilot report higher satisfaction with their jobs than those who don’t and are up to 55% more productive at writing code without sacrifice to quality, which all adds up to engaged developers shipping great software faster. Without sacrifice to quality, which all adds up to engaged developers shipping great software faster.

GitHub Copilot integrates with leading editors, including Visual Studio Code, Visual Studio, JetBrains IDEs, and Neovim, and, unlike other AI coding assistants, is natively built into GitHub. Growing to millions of individual users and tens of thousands of business customers, Copilot is the world’s most widely adopted AI developer tool and the competitive advantage developers ask for by name.

For more information about the general use of GitHub Copilot, product impact, human, oversight, and privacy, see the comprehensive list of  GitHub Copilot FAQs.

GitHub Copilot is trained on all languages that appear in public repositories. For each language, the quality of suggestions you receive may depend on the volume and diversity of training data for that language. For example, JavaScript is well-represented in public repositories and is one of GitHub Copilot’s best supported languages. Languages with less representation in public repositories may produce fewer or less robust suggestions.

For more information about the general use of GitHub Copilot, product impact, human, oversight, and privacy, see the comprehensive list of  GitHub Copilot FAQs.

GitHub Copilot is powered by generative AI models developed by GitHub, OpenAI, and Microsoft. It has been trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub.

For more information about the general use of GitHub Copilot, product impact, human, oversight, and privacy, see the comprehensive list of  GitHub Copilot FAQs.

When thinking about intellectual property and open source issues, it is critical to understand how GitHub Copilot really works. The AI models that create Copilot’s suggestions may be trained on public code, but do not contain any code. When they generate a suggestion, they are not “copying and pasting” from any codebase.

• To generate a code suggestion, the Copilot extension begins by examining the code in your editor—focusing on the lines just before and after your cursor, but also information including other files open in your editor and the URLs of repositories or file paths to identify relevant context. That information is sent to Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions.
• To generate a suggestion for chat in the code editor, the Copilot extension creates a contextual prompt by combining your prompt with additional context including the code file open in your active document, your code selection, and general workspace information, such as frameworks, languages, and dependencies. That information is sent to Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions.
• To generate a suggestion for chat on GitHub.com, such as providing an answer to a question from your chat prompt, Copilot creates a contextual prompt by combining your prompt with additional context including previous prompts, the open pages on GitHub.com as well as retrieved context from your codebase or Bing search. That information is sent to Copilot’s model, to make a probabilistic determination of what is likely to come next and generate suggestions.

For more information about the general use of GitHub Copilot, product impact, human, oversight, and privacy, see the comprehensive list of  GitHub Copilot FAQs.

• User Engagement Data: This includes pseudonymous identifiers captured on user interactions with Copilot, such as accepted or dismissed completions, error messages, system logs, and product usage metrics.
• Prompts: These are inputs for chat or code, along with context, sent to Copilot’s AI to generate suggestions.
• Suggestions: These are the AI-generated code lines or chat responses provided to users based on their prompts.
• Feedback Data: This comprises real-time user feedback, including reactions (e.g., thumbs up/down) and optional comments, along with feedback from support tickets.

For more information about the general use of GitHub Copilot, product impact, human, oversight, and privacy, see the comprehensive list of  GitHub Copilot FAQs.

How GitHub uses Copilot data depends on how the user accesses Copilot and for what purpose. Users can access GitHub Copilot through the web, extensions, mobile apps, computer terminal, and various IDEs (Integrated Development Environments). GitHub generally uses personal data to:

• Deliver, maintain, and update the services as per the customer’s configuration and usage, to ensure personalized experiences and recommendations.
• Troubleshoot, which involves preventing, detecting, resolving, and mitigating issues, including security incidents and product-related problems, by fixing software bugs and maintaining the online services’ functionality and up-to-dateness.
• Enhance user productivity, reliability, effectiveness, quality, privacy, accessibility, and security by keeping the service current and operational.

These practices are outlined in GitHub’s Data Protection Agreement (DPA), which details our data handling commitments to our data controller customers.

GitHub also uses certain personal data with customer authorization under the DPA, for the following purposes:

• Billing and account management.
• To comply with and resolve legal obligations.
• For abuse detection, prevention, and protection, virus scanning, and scanning to detect violations of terms of service.
• To generate summary reports for calculating employee commissions and partner incentives.
• To produce aggregated reports for internal use and strategic planning, covering areas like forecasting, revenue analysis, capacity planning, and product strategy.

For details on GitHub’s data processing activities as a controller, particularly for Copilot Individual customers, refer to the GitHub Privacy Statement.

The primary IP considerations for GitHub Copilot relate to copyright. The model that powers Copilot is trained on a broad collection of publicly accessible code, which may include copyrighted code, and Copilot’s suggestions (in rare instances) may resemble the code its model was trained on. Here’s some basic information you should know about these considerations:

Copyright law permits the use of copyrighted works to train AI models: Countries around the world have provisions in their copyright laws that enable machines to learn, understand, extract patterns, and facts from copyrighted materials, including software code. For example, the European Union, Japan, and Singapore, have express provisions permitting machine learning to develop AI models. Other countries including Canada, India, and the United States also permit such training under their fair use/fair dealing provisions. GitHub Copilot’s AI model was trained with the use of code from GitHub’s public repositories—which are publicly accessible and within the scope of permissible copyright use.

What about copyright risk in suggestions? In rare instances (less than 1% based on GitHub’s research), suggestions from GitHub may match examples of code used to train GitHub’s AI model. Again, Copilot does not “look up” or “copy and paste” code, but is instead using context from a user’s workspace to synthesize and generate a suggestion.

Our experience shows that matching suggestions are most likely to occur in two situations: (i) when there is little or no context in the code editor for Copilot’s model to synthesize, or (ii) when a matching suggestion represents a common approach or method. If a code suggestion matches existing code, there is risk that using that suggestion could trigger claims of copyright infringement, which would depend on the amount and nature of code used, and the context of how the code is used. In many ways, this is the same risk that arises when using any code that a developer does not originate, such as copying code from an online source, or reusing code from a library. That is why responsible organizations and developers recommend that users employ code scanning policies to identify and evaluate potential matching code.

In Copilot, you can opt whether to allow Copilot to suggest code completions that match publicly available code on GitHub.com. For more information, see “Configuring GitHub Copilot settings on GitHub.com”. If you have allowed suggestions that match public code, GitHub Copilot can provide you with details about the matching code when you accept such suggestions. Matching code does not necessarily mean copyright infringement, so it is ultimately up to the user to determine whether to use the suggestion, and what and who to attribute (along with other license compliance) in appropriate circumstances.

For more information about the general use of GitHub Copilot, product impact, human, oversight, and privacy, see the comprehensive list of  GitHub Copilot FAQs.