About GitHub Copilot Chat
GitHub Copilot Chat is a chat interface that lets you interact with GitHub Copilot, to ask and receive answers to coding-related questions within the GitHub website and supported IDEs. The chat interface provides access to coding information and support without requiring you to navigate documentation or search online forums. For more information, see "What is GitHub Copilot?."
GitHub Copilot Chat can answer a wide range of coding-related questions on topics including syntax, programming concepts, test cases, debugging, and more. GitHub Copilot Chat is not designed to answer non-coding questions or provide general information on topics outside of coding.
The primary supported language for GitHub Copilot Chat is English.
GitHub Copilot Chat works by using a combination of natural language processing and machine learning to understand your question and provide you with an answer. This process can be broken down into a number of steps.
Input processing
The input prompt from the user is pre-processed by the Copilot Chat system, combined with contextual information (for example, the name of the repository the user is currently viewing and the files the user has open), and sent to a large language model. User input can take the form of code snippets or plain language.
Addtional contextual information can be automatically added to chat prompts from an optional .github/copilot-instructions.md file that the user can create. This additional information is not displayed in the chat, but if the instructions are sent to the model, the file is listed as a reference in the chat response. The user can disable this feature in the Copilot extension settings.
The large language model will take the prompt, gather additional context (for example repository data stored on GitHub), and provide a response based on the prompt. The system is only intended to respond to coding-related questions.
Language model analysis
The pre-processed prompt is then passed through the Copilot Chat language model, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt.
Response generation
The language model generates a response based on its analysis of the input prompt and the context provided to it. The language model can gather additional context and provide a response based on the prompt. This response can take the form of generated code, code suggestions, or explanations of existing code.
When you use the @github chat participant, Copilot Chat will be able to gather context from your code stored on GitHub and search results from Bing (if enabled by your administrator).
Output formatting
The response generated by Copilot Chat is formatted and presented to the user. Copilot Chat may use syntax highlighting, indentation, and other formatting features to add clarity to the generated response. Depending upon the type of question from the user, links to context that the model used when generating a response, such as source code files or documentation, may also be provided.
GitHub Copilot Chat is intended to provide you with the most relevant answer to your question. However, it may not always provide the answer you are looking for. Users of Copilot Chat are responsible for reviewing and validating responses generated by the system to ensure they are accurate and appropriate. Additionally, as part of our product development process, we undertake red teaming to understand and improve the safety of Copilot Chat. Input prompts and output completions are run through content filters. The content filtering system detects and prevents the output on specific categories of content including harmful, offensive, or off-topic content. For more information on improving the performance of GitHub Copilot Chat, see "Improving performance for GitHub Copilot Chat."
Leveraging a web search to answer a question
Note
Bing search integration in Copilot Chat is currently in public preview and is subject to change.
When you use the @github chat participant, GitHub Copilot Chat can use a Bing search to help answer your question if this has been enabled by your administrator.
Your GitHub Enterprise administrator can enable Bing for your whole enterprise, or can delegate this decision to the organizational administrator. For more information, see "Enforcing policies for GitHub Copilot in your enterprise."
When leveraging Bing, Copilot will use the content of your prompt, as well as additional available context, to generate a Bing search query on your behalf that is sent to the Bing Search API. Copilot will provide a link to the search results with its response. The search query sent to Bing is governed by Microsoft's Privacy Statement.
Use cases for GitHub Copilot Chat
GitHub Copilot Chat can provide coding assistance in a variety of scenarios.
Generating unit test cases
Copilot Chat can help you write unit test cases by generating code snippets based on the code open in the editor or the code snippet you highlight in the editor. This may help you write test cases without spending as much time on repetitive tasks. For example, if you are writing a test case for a specific function, you can use Copilot Chat to suggest possible input parameters and expected output values based on the function's signature and body. Copilot Chat can also suggest assertions that ensure the function is working correctly, based on the code's context and semantics.
Copilot Chat can also help you write test cases for edge cases and boundary conditions that might be difficult to identify manually. For instance, Copilot Chat can suggest test cases for error handling, null values, or unexpected input types, helping you ensure your code is robust and resilient. However, it is important to note that generated test cases may not cover all possible scenarios, and manual testing and code review are still necessary to ensure the quality of the code. For more information on generating unit test cases, see "Asking GitHub Copilot Chat questions about your code."
Explaining code and suggesting improvements
Copilot Chat can help explain selected code by generating natural language descriptions of the code's functionality and purpose. This can be useful if you want to understand the code's behavior or for non-technical stakeholders who need to understand how the code works. For example, if you select a function or code block in the code editor, Copilot Chat can generate a natural language description of what the code does and how it fits into the overall system. This can include information such as the function's input and output parameters, its dependencies, and its purpose in the larger application.
Copilot Chat can also suggest potential improvements to selected code, such as improved handling of errors and edge cases, or changes to the logical flow to make the code more readable.
By generating explanations and suggesting related documentation, Copilot Chat may help you to understand the selected code, leading to improved collaboration and more effective software development. However, it's important to note that the generated explanations and documentation may not always be accurate or complete, so you'll need to review, and occasionally correct, Copilot Chat's output.
Proposing code fixes
Copilot Chat can propose a fix for bugs in your code by suggesting code snippets and solutions based on the context of the error or issue. This can be useful if you are struggling to identify the root cause of a bug or you need guidance on the best way to fix it. For example, if your code produces an error message or warning, Copilot Chat can suggest possible fixes based on the error message, the code's syntax, and the surrounding code.
Copilot Chat can suggest changes to variables, control structures, or function calls that might resolve the issue and generate code snippets that can be incorporated into the codebase. However, it's important to note that the suggested fixes may not always be optimal or complete, so you'll need to review and test the suggestions.
Answering coding questions
You can ask Copilot Chat for help or clarification on specific coding problems and receive responses in natural language format or in code snippet format.
The response generated by Copilot Chat will use the model's training data set to answer your questions.
When you use the @github chat participant, the response generated may additionally use search results from Bing, code in your repositories, and Markdown documentation in your knowledge bases.
This can be a useful tool for programmers, as it can provide guidance and support for common coding tasks and challenges.
Improving performance for GitHub Copilot Chat
Copilot Chat can support a wide range of practical applications like code generation, code analysis, and code fixes, each with different performance metrics and mitigation strategies. To enhance performance and address some of the the limitations of Copilot Chat, there are various measures that you can adopt. For more information on the limitations of Copilot Chat, see "Limitations of GitHub Copilot Chat."
Keep your prompts on topic
Copilot Chat is intended to address queries related to coding exclusively. Therefore, limiting the prompt to coding questions or tasks can enhance the model's output quality.
Use Copilot Chat as a tool, not a replacement
While Copilot Chat can be a powerful tool for generating code, it is important to use it as a tool rather than a replacement for human programming. You should always review and test the code generated by Copilot Chat to ensure that it meets your requirements and is free of errors or security concerns.
Use secure coding and code review practices
While Copilot Chat can generate syntactically correct code, it may not always be secure. You should always follow best practices for secure coding, such as avoiding hard-coded passwords or SQL injection vulnerabilities, as well as following code review best practices, to address Copilot Chat's limitations.
Provide feedback
If you encounter any issues or limitations with Copilot Chat, we recommend that you provide feedback through the share feedback link in the Copilot Chat interface of your IDE. This can help the developers to improve the tool and address any concerns or limitations.
Stay up to date
Copilot Chat is a new technology and is likely to evolve over time. You should stay up to date with any updates or changes to the tool, as well as any new security risks or best practices that may emerge. Automated extension updates are enabled by default in Visual Studio Code, Visual Studio, and the JetBrains suite of IDEs. For GitHub Copilot Chat in GitHub you will always have access to the latest product experience. If you have automatic updates enabled, Copilot Chat will automatically update to the latest version when you open your IDE. For more information on automatic updates in your IDE, see the Visual Studio Code documentation, the Visual Studio documentation and the documentation for your JetBrains IDE.
Limitations of GitHub Copilot Chat
Depending on factors such as your codebase and input data, you may experience different levels of performance when using Copilot Chat. The following information is designed to help you understand system limitations and key concepts about performance as they apply to Copilot Chat.
Limited scope
Copilot Chat has been trained on a large body of code but still has a limited scope and may not be able to handle more complex code structures or obscure programming languages. 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 be more challenging for Copilot Chat to provide assistance with. Additionally, Copilot Chat can only suggest code based on the context of the code being written, so it may not be able to identify larger design or architectural issues.
Potential biases
Copilot's training data is drawn from existing code repositories, which may contain biases and errors that can be perpetuated by the tool. Additionally, Copilot Chat may be biased towards certain programming languages or coding styles, which can lead to suboptimal or incomplete code suggestions.
Security risks
Copilot Chat generates code based on the context of the code being written, which can potentially expose sensitive information or vulnerabilities if not used carefully. You should be careful when using Copilot Chat to generate code for security-sensitive applications and always review and test the generated code thoroughly.
Matches with public code
Copilot Chat is capable of generating new code, which it does in a probabilistic way. While the probability that it may produce code that matches code in the training set is low, a Copilot Chat suggestion may contain some code snippets that match code in the training set.
If you have disabled suggestions that match public code then Copilot Chat utilizes filters that prevent it from showing code that matches code found in public repositories on GitHub. However, you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
If you have enabled suggestions that match public code then Copilot Chat displays a message if matching code is found. The message includes a link that allows you to show the details of the matched code, and any license details that were found, in the VS Code editor. For more information, see "Finding public code that matches GitHub Copilot suggestions."
Copilot Chat utilizes filters that block matches with public code on GitHub repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
Copilot Chat utilizes filters that block matches with public code on GitHub repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
Inaccurate code
One of the limitations of Copilot Chat is that it may generate code that appears to be valid but may not actually be semantically or syntactically correct or may not accurately reflect the intent of the developer. To mitigate the risk of inaccurate code, you should carefully review and test the generated code, particularly when dealing with critical or sensitive applications. You should also ensure that the generated code adheres to best practices and design patterns and fits within the overall architecture and style of the codebase.
Inaccurate responses to non-coding topics
Copilot Chat is not designed to answer non-coding questions, and therefore its responses may not always be accurate or helpful in these contexts. If a user asks Copilot Chat a non-coding question, it may generate an answer that is irrelevant or nonsensical, or it may simply indicate that it is unable to provide a useful response.
Next steps
For details of how to use GitHub Copilot Chat, see:
Further reading
About GitHub Copilot Chat
GitHub Copilot Chat is a chat interface that lets you interact with GitHub Copilot, to ask and receive answers to coding-related questions within the GitHub website and supported IDEs. The chat interface provides access to coding information and support without requiring you to navigate documentation or search online forums. For more information, see "What is GitHub Copilot?."
GitHub Copilot Chat can answer a wide range of coding-related questions on topics including syntax, programming concepts, test cases, debugging, and more. GitHub Copilot Chat is not designed to answer non-coding questions or provide general information on topics outside of coding.
The primary supported language for GitHub Copilot Chat is English.
GitHub Copilot Chat works by using a combination of natural language processing and machine learning to understand your question and provide you with an answer. This process can be broken down into a number of steps.
Input processing
The input prompt from the user is pre-processed by the Copilot Chat system, combined with contextual information (for example, the name of the repository the user is currently viewing and the files the user has open), and sent to a large language model. User input can take the form of code snippets or plain language.
Addtional contextual information can be automatically added to chat prompts from an optional .github/copilot-instructions.md file that the user can create. This additional information is not displayed in the chat, but if the instructions are sent to the model, the file is listed as a reference in the chat response. The user can disable this feature in the Copilot extension settings.
The large language model will take the prompt, gather additional context (for example repository data stored on GitHub), and provide a response based on the prompt. The system is only intended to respond to coding-related questions.
Language model analysis
The pre-processed prompt is then passed through the Copilot Chat language model, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt.
Response generation
The language model generates a response based on its analysis of the input prompt and the context provided to it. The language model can gather additional context and provide a response based on the prompt. This response can take the form of generated code, code suggestions, or explanations of existing code.
When you use the @github chat participant, Copilot Chat will be able to gather context from your code stored on GitHub and search results from Bing (if enabled by your administrator).
Output formatting
The response generated by Copilot Chat is formatted and presented to the user. Copilot Chat may use syntax highlighting, indentation, and other formatting features to add clarity to the generated response. Depending upon the type of question from the user, links to context that the model used when generating a response, such as source code files or documentation, may also be provided.
GitHub Copilot Chat is intended to provide you with the most relevant answer to your question. However, it may not always provide the answer you are looking for. Users of Copilot Chat are responsible for reviewing and validating responses generated by the system to ensure they are accurate and appropriate. Additionally, as part of our product development process, we undertake red teaming to understand and improve the safety of Copilot Chat. Input prompts and output completions are run through content filters. The content filtering system detects and prevents the output on specific categories of content including harmful, offensive, or off-topic content. For more information on improving the performance of GitHub Copilot Chat, see "Improving performance for GitHub Copilot Chat."
Leveraging a web search to answer a question
Note
Bing search integration in Copilot Chat is currently in public preview and is subject to change.
When you use the @github chat participant, GitHub Copilot Chat can use a Bing search to help answer your question if this has been enabled by your administrator.
Your GitHub Enterprise administrator can enable Bing for your whole enterprise, or can delegate this decision to the organizational administrator. For more information, see "Enforcing policies for GitHub Copilot in your enterprise."
When leveraging Bing, Copilot will use the content of your prompt, as well as additional available context, to generate a Bing search query on your behalf that is sent to the Bing Search API. Copilot will provide a link to the search results with its response. The search query sent to Bing is governed by Microsoft's Privacy Statement.
Use cases for GitHub Copilot Chat
GitHub Copilot Chat can provide coding assistance in a variety of scenarios.
Generating unit test cases
Copilot Chat can help you write unit test cases by generating code snippets based on the code open in the editor or the code snippet you highlight in the editor. This may help you write test cases without spending as much time on repetitive tasks. For example, if you are writing a test case for a specific function, you can use Copilot Chat to suggest possible input parameters and expected output values based on the function's signature and body. Copilot Chat can also suggest assertions that ensure the function is working correctly, based on the code's context and semantics.
Copilot Chat can also help you write test cases for edge cases and boundary conditions that might be difficult to identify manually. For instance, Copilot Chat can suggest test cases for error handling, null values, or unexpected input types, helping you ensure your code is robust and resilient. However, it is important to note that generated test cases may not cover all possible scenarios, and manual testing and code review are still necessary to ensure the quality of the code. For more information on generating unit test cases, see "Asking GitHub Copilot Chat questions about your code."
Explaining code and suggesting improvements
Copilot Chat can help explain selected code by generating natural language descriptions of the code's functionality and purpose. This can be useful if you want to understand the code's behavior or for non-technical stakeholders who need to understand how the code works. For example, if you select a function or code block in the code editor, Copilot Chat can generate a natural language description of what the code does and how it fits into the overall system. This can include information such as the function's input and output parameters, its dependencies, and its purpose in the larger application.
Copilot Chat can also suggest potential improvements to selected code, such as improved handling of errors and edge cases, or changes to the logical flow to make the code more readable.
By generating explanations and suggesting related documentation, Copilot Chat may help you to understand the selected code, leading to improved collaboration and more effective software development. However, it's important to note that the generated explanations and documentation may not always be accurate or complete, so you'll need to review, and occasionally correct, Copilot Chat's output.
Proposing code fixes
Copilot Chat can propose a fix for bugs in your code by suggesting code snippets and solutions based on the context of the error or issue. This can be useful if you are struggling to identify the root cause of a bug or you need guidance on the best way to fix it. For example, if your code produces an error message or warning, Copilot Chat can suggest possible fixes based on the error message, the code's syntax, and the surrounding code.
Copilot Chat can suggest changes to variables, control structures, or function calls that might resolve the issue and generate code snippets that can be incorporated into the codebase. However, it's important to note that the suggested fixes may not always be optimal or complete, so you'll need to review and test the suggestions.
Answering coding questions
You can ask Copilot Chat for help or clarification on specific coding problems and receive responses in natural language format or in code snippet format.
The response generated by Copilot Chat will use the model's training data set to answer your questions.
This can be a useful tool for programmers, as it can provide guidance and support for common coding tasks and challenges.
Improving performance for GitHub Copilot Chat
Copilot Chat can support a wide range of practical applications like code generation, code analysis, and code fixes, each with different performance metrics and mitigation strategies. To enhance performance and address some of the the limitations of Copilot Chat, there are various measures that you can adopt. For more information on the limitations of Copilot Chat, see "Limitations of GitHub Copilot Chat."
Keep your prompts on topic
Copilot Chat is intended to address queries related to coding exclusively. Therefore, limiting the prompt to coding questions or tasks can enhance the model's output quality.
Use Copilot Chat as a tool, not a replacement
While Copilot Chat can be a powerful tool for generating code, it is important to use it as a tool rather than a replacement for human programming. You should always review and test the code generated by Copilot Chat to ensure that it meets your requirements and is free of errors or security concerns.
Use secure coding and code review practices
While Copilot Chat can generate syntactically correct code, it may not always be secure. You should always follow best practices for secure coding, such as avoiding hard-coded passwords or SQL injection vulnerabilities, as well as following code review best practices, to address Copilot Chat's limitations.
Provide feedback
If you encounter any issues or limitations with Copilot Chat, we recommend that you provide feedback through the share feedback link in the Copilot Chat interface of your IDE. This can help the developers to improve the tool and address any concerns or limitations.
Stay up to date
Copilot Chat is a new technology and is likely to evolve over time. You should stay up to date with any updates or changes to the tool, as well as any new security risks or best practices that may emerge. Automated extension updates are enabled by default in Visual Studio Code, Visual Studio, and the JetBrains suite of IDEs. For GitHub Copilot Chat in GitHub you will always have access to the latest product experience. If you have automatic updates enabled, Copilot Chat will automatically update to the latest version when you open your IDE. For more information on automatic updates in your IDE, see the Visual Studio Code documentation, the Visual Studio documentation and the documentation for your JetBrains IDE.
Limitations of GitHub Copilot Chat
Depending on factors such as your codebase and input data, you may experience different levels of performance when using Copilot Chat. The following information is designed to help you understand system limitations and key concepts about performance as they apply to Copilot Chat.
Limited scope
Copilot Chat has been trained on a large body of code but still has a limited scope and may not be able to handle more complex code structures or obscure programming languages. 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 be more challenging for Copilot Chat to provide assistance with. Additionally, Copilot Chat can only suggest code based on the context of the code being written, so it may not be able to identify larger design or architectural issues.
Potential biases
Copilot's training data is drawn from existing code repositories, which may contain biases and errors that can be perpetuated by the tool. Additionally, Copilot Chat may be biased towards certain programming languages or coding styles, which can lead to suboptimal or incomplete code suggestions.
Security risks
Copilot Chat generates code based on the context of the code being written, which can potentially expose sensitive information or vulnerabilities if not used carefully. You should be careful when using Copilot Chat to generate code for security-sensitive applications and always review and test the generated code thoroughly.
Matches with public code
Copilot Chat is capable of generating new code, which it does in a probabilistic way. While the probability that it may produce code that matches code in the training set is low, a Copilot Chat suggestion may contain some code snippets that match code in the training set.
If you have disabled suggestions that match public code then Copilot Chat utilizes filters that prevent it from showing code that matches code found in public repositories on GitHub. However, you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
If you have enabled suggestions that match public code then Copilot Chat displays a message if matching code is found. The message includes a link that allows you to show the details of the matched code, and any license details that were found, in the VS Code editor. For more information, see "Finding public code that matches GitHub Copilot suggestions."
Copilot Chat utilizes filters that block matches with public code on GitHub repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
Copilot Chat utilizes filters that block matches with public code on GitHub repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
Inaccurate code
One of the limitations of Copilot Chat is that it may generate code that appears to be valid but may not actually be semantically or syntactically correct or may not accurately reflect the intent of the developer. To mitigate the risk of inaccurate code, you should carefully review and test the generated code, particularly when dealing with critical or sensitive applications. You should also ensure that the generated code adheres to best practices and design patterns and fits within the overall architecture and style of the codebase.
Inaccurate responses to non-coding topics
Copilot Chat is not designed to answer non-coding questions, and therefore its responses may not always be accurate or helpful in these contexts. If a user asks Copilot Chat a non-coding question, it may generate an answer that is irrelevant or nonsensical, or it may simply indicate that it is unable to provide a useful response.
Next steps
For details of how to use GitHub Copilot Chat, see:
Further reading
About GitHub Copilot Chat
GitHub Copilot Chat is a chat interface that lets you interact with GitHub Copilot, to ask and receive answers to coding-related questions within the GitHub website and supported IDEs. The chat interface provides access to coding information and support without requiring you to navigate documentation or search online forums. For more information, see "What is GitHub Copilot?."
GitHub Copilot Chat can answer a wide range of coding-related questions on topics including syntax, programming concepts, test cases, debugging, and more. GitHub Copilot Chat is not designed to answer non-coding questions or provide general information on topics outside of coding.
The primary supported language for GitHub Copilot Chat is English.
GitHub Copilot Chat works by using a combination of natural language processing and machine learning to understand your question and provide you with an answer. This process can be broken down into a number of steps.
Input processing
The input prompt from the user is pre-processed by the Copilot Chat system, combined with contextual information (for example, the name of the repository the user is currently viewing and the files the user has open), and sent to a large language model. User input can take the form of code snippets or plain language.
The large language model will take the prompt, gather additional context (for example repository data stored on GitHub), and provide a response based on the prompt. The system is only intended to respond to coding-related questions.
Language model analysis
The pre-processed prompt is then passed through the Copilot Chat language model, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt.
Response generation
The language model generates a response based on its analysis of the input prompt and the context provided to it. The language model can gather additional context and provide a response based on the prompt. This response can take the form of generated code, code suggestions, or explanations of existing code.
Output formatting
The response generated by Copilot Chat is formatted and presented to the user. Copilot Chat may use syntax highlighting, indentation, and other formatting features to add clarity to the generated response. Depending upon the type of question from the user, links to context that the model used when generating a response, such as source code files or documentation, may also be provided.
GitHub Copilot Chat is intended to provide you with the most relevant answer to your question. However, it may not always provide the answer you are looking for. Users of Copilot Chat are responsible for reviewing and validating responses generated by the system to ensure they are accurate and appropriate. Additionally, as part of our product development process, we undertake red teaming to understand and improve the safety of Copilot Chat. Input prompts and output completions are run through content filters. The content filtering system detects and prevents the output on specific categories of content including harmful, offensive, or off-topic content. For more information on improving the performance of GitHub Copilot Chat, see "Improving performance for GitHub Copilot Chat."
Use cases for GitHub Copilot Chat
GitHub Copilot Chat can provide coding assistance in a variety of scenarios.
Generating unit test cases
Copilot Chat can help you write unit test cases by generating code snippets based on the code open in the editor or the code snippet you highlight in the editor. This may help you write test cases without spending as much time on repetitive tasks. For example, if you are writing a test case for a specific function, you can use Copilot Chat to suggest possible input parameters and expected output values based on the function's signature and body. Copilot Chat can also suggest assertions that ensure the function is working correctly, based on the code's context and semantics.
Copilot Chat can also help you write test cases for edge cases and boundary conditions that might be difficult to identify manually. For instance, Copilot Chat can suggest test cases for error handling, null values, or unexpected input types, helping you ensure your code is robust and resilient. However, it is important to note that generated test cases may not cover all possible scenarios, and manual testing and code review are still necessary to ensure the quality of the code. For more information on generating unit test cases, see "Asking GitHub Copilot Chat questions about your code."
Explaining code and suggesting improvements
Copilot Chat can help explain selected code by generating natural language descriptions of the code's functionality and purpose. This can be useful if you want to understand the code's behavior or for non-technical stakeholders who need to understand how the code works. For example, if you select a function or code block in the code editor, Copilot Chat can generate a natural language description of what the code does and how it fits into the overall system. This can include information such as the function's input and output parameters, its dependencies, and its purpose in the larger application.
Copilot Chat can also suggest potential improvements to selected code, such as improved handling of errors and edge cases, or changes to the logical flow to make the code more readable.
By generating explanations and suggesting related documentation, Copilot Chat may help you to understand the selected code, leading to improved collaboration and more effective software development. However, it's important to note that the generated explanations and documentation may not always be accurate or complete, so you'll need to review, and occasionally correct, Copilot Chat's output.
Proposing code fixes
Copilot Chat can propose a fix for bugs in your code by suggesting code snippets and solutions based on the context of the error or issue. This can be useful if you are struggling to identify the root cause of a bug or you need guidance on the best way to fix it. For example, if your code produces an error message or warning, Copilot Chat can suggest possible fixes based on the error message, the code's syntax, and the surrounding code.
Copilot Chat can suggest changes to variables, control structures, or function calls that might resolve the issue and generate code snippets that can be incorporated into the codebase. However, it's important to note that the suggested fixes may not always be optimal or complete, so you'll need to review and test the suggestions.
Answering coding questions
You can ask Copilot Chat for help or clarification on specific coding problems and receive responses in natural language format or in code snippet format.
The response generated by Copilot Chat will use the model's training data set to answer your questions.
This can be a useful tool for programmers, as it can provide guidance and support for common coding tasks and challenges.
Improving performance for GitHub Copilot Chat
Copilot Chat can support a wide range of practical applications like code generation, code analysis, and code fixes, each with different performance metrics and mitigation strategies. To enhance performance and address some of the the limitations of Copilot Chat, there are various measures that you can adopt. For more information on the limitations of Copilot Chat, see "Limitations of GitHub Copilot Chat."
Keep your prompts on topic
Copilot Chat is intended to address queries related to coding exclusively. Therefore, limiting the prompt to coding questions or tasks can enhance the model's output quality.
Use Copilot Chat as a tool, not a replacement
While Copilot Chat can be a powerful tool for generating code, it is important to use it as a tool rather than a replacement for human programming. You should always review and test the code generated by Copilot Chat to ensure that it meets your requirements and is free of errors or security concerns.
Use secure coding and code review practices
While Copilot Chat can generate syntactically correct code, it may not always be secure. You should always follow best practices for secure coding, such as avoiding hard-coded passwords or SQL injection vulnerabilities, as well as following code review best practices, to address Copilot Chat's limitations.
Provide feedback
If you encounter any issues or limitations with Copilot Chat, we recommend that you provide feedback through the share feedback link in the Copilot Chat interface of your IDE. This can help the developers to improve the tool and address any concerns or limitations.
Stay up to date
Copilot Chat is a new technology and is likely to evolve over time. You should stay up to date with any updates or changes to the tool, as well as any new security risks or best practices that may emerge. Automated extension updates are enabled by default in Visual Studio Code, Visual Studio, and the JetBrains suite of IDEs. For GitHub Copilot Chat in GitHub you will always have access to the latest product experience. If you have automatic updates enabled, Copilot Chat will automatically update to the latest version when you open your IDE. For more information on automatic updates in your IDE, see the Visual Studio Code documentation, the Visual Studio documentation and the documentation for your JetBrains IDE.
Limitations of GitHub Copilot Chat
Depending on factors such as your codebase and input data, you may experience different levels of performance when using Copilot Chat. The following information is designed to help you understand system limitations and key concepts about performance as they apply to Copilot Chat.
Limited scope
Copilot Chat has been trained on a large body of code but still has a limited scope and may not be able to handle more complex code structures or obscure programming languages. 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 be more challenging for Copilot Chat to provide assistance with. Additionally, Copilot Chat can only suggest code based on the context of the code being written, so it may not be able to identify larger design or architectural issues.
Potential biases
Copilot's training data is drawn from existing code repositories, which may contain biases and errors that can be perpetuated by the tool. Additionally, Copilot Chat may be biased towards certain programming languages or coding styles, which can lead to suboptimal or incomplete code suggestions.
Security risks
Copilot Chat generates code based on the context of the code being written, which can potentially expose sensitive information or vulnerabilities if not used carefully. You should be careful when using Copilot Chat to generate code for security-sensitive applications and always review and test the generated code thoroughly.
Matches with public code
Copilot Chat is capable of generating new code, which it does in a probabilistic way. While the probability that it may produce code that matches code in the training set is low, a Copilot Chat suggestion may contain some code snippets that match code in the training set.
If you have disabled suggestions that match public code then Copilot Chat utilizes filters that prevent it from showing code that matches code found in public repositories on GitHub. However, you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
If you have enabled suggestions that match public code then Copilot Chat displays a message if matching code is found. The message includes a link that allows you to show the details of the matched code, and any license details that were found, in the VS Code editor. For more information, see "Finding public code that matches GitHub Copilot suggestions."
Copilot Chat utilizes filters that block matches with public code on GitHub repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
Copilot Chat utilizes filters that block matches with public code on GitHub repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
Inaccurate code
One of the limitations of Copilot Chat is that it may generate code that appears to be valid but may not actually be semantically or syntactically correct or may not accurately reflect the intent of the developer. To mitigate the risk of inaccurate code, you should carefully review and test the generated code, particularly when dealing with critical or sensitive applications. You should also ensure that the generated code adheres to best practices and design patterns and fits within the overall architecture and style of the codebase.
Inaccurate responses to non-coding topics
Copilot Chat is not designed to answer non-coding questions, and therefore its responses may not always be accurate or helpful in these contexts. If a user asks Copilot Chat a non-coding question, it may generate an answer that is irrelevant or nonsensical, or it may simply indicate that it is unable to provide a useful response.
Next steps
For details of how to use GitHub Copilot Chat, see: