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Responsible use of GitHub Copilot Chat in GitHub |
Chat in GitHub |
Learn how to use {% data variables.product.prodname_copilot_chat_dotcom %} responsibly by understanding its purposes, capabilities, and limitations. |
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rai |
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[!NOTE] {% data reusables.rai.copilot.copilot-chat-dotcom-beta %}
{% data variables.product.prodname_copilot_chat_dotcom %} is a chat interface that lets you interact with {% data variables.product.prodname_copilot %}, to ask and receive answers to coding-related questions within {% data variables.product.github %}.
The chat interface provides access to coding information and support without requiring you to navigate documentation or search online forums.
[!NOTE] {% data variables.product.prodname_copilot_chat_short %} is also available in {% data variables.product.prodname_vscode %}, {% data variables.product.prodname_vs %}, and the JetBrains suite of IDEs. However, features available in these IDEs differ from features available on {% data variables.product.github %}.
{% data variables.product.prodname_copilot_chat %} can answer a wide range of coding-related questions on topics including syntax, programming concepts, test cases, debugging, and more. {% data variables.product.prodname_copilot_chat %} is not designed to answer non-coding questions or provide general information on topics outside of coding.
The primary supported language for {% data variables.product.prodname_copilot_chat_dotcom_short %} is English.
{% data variables.product.prodname_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.
The input prompt from the user is pre-processed by the {% data variables.product.prodname_copilot_chat_short %} system, combined with contextual information (for example, the current date and time and the name of the repository the user is currently viewing), 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 {% data variables.product.prodname_dotcom %} or search results from Bing), and provide a response based on the prompt. The system is only intended to respond to coding-related questions.
The pre-processed prompt is then passed through the {% data variables.product.prodname_copilot_chat_short %} 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.
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 (for example repository data stored on {% data variables.product.prodname_dotcom %} or search results from Bing), and provide a response based on the prompt. This response can take the form of generated code, code suggestions, or explanations of existing code.
The response generated by {% data variables.product.prodname_copilot_chat_short %} is formatted and presented to the user. {% data variables.product.prodname_copilot_chat_short %} 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, issues, Bing search results, or documentation, may also be provided.
{% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot_chat_short %}. 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 {% data variables.product.prodname_copilot_chat_short %}, see "Improving performance for {% data variables.product.prodname_copilot_chat_short %}."
{% data variables.product.prodname_copilot_chat_short %} can provide coding assistance in a variety of scenarios.
You can ask {% data variables.product.prodname_copilot_chat_short %} for help or clarification on specific coding problems and receive responses in natural language format or in code snippet format.
The response generated by {% data variables.product.prodname_copilot_chat_short %} may use the model's training data set, search results from Bing, code in your repositories, and Markdown documentation in your knowledge bases 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.
{% data variables.product.prodname_copilot_chat_short %} 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, {% data variables.product.prodname_copilot_chat_short %} 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.
{% data variables.product.prodname_copilot_chat_short %} 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, {% data variables.product.prodname_copilot_chat_short %} 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, {% data variables.product.prodname_copilot_chat_short %}'s output.
{% data variables.product.prodname_copilot_chat_short %} 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, {% data variables.product.prodname_copilot_chat_short %} can suggest possible fixes based on the error message, the code's syntax, and the surrounding code.
{% data variables.product.prodname_copilot_chat_short %} 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.
{% data variables.product.prodname_copilot_chat_short %} can read a {% data variables.product.prodname_dotcom %} issue and summarize it, answer questions about it, or propose next steps. This can be useful if you have a long, complex issue with many comments, and you want to understand it quickly or figure out what to do next.
However, it's important to note that {% data variables.product.prodname_copilot_chat_short %}'s answers and summaries may not always be accurate or complete, so you'll need to review {% data variables.product.prodname_copilot_chat_short %}'s output for accuracy.
{% data variables.product.prodname_copilot_chat_short %} can help you find out what changed in a specific release, it can summarize the information in a discussion, and it can explain the changes in a specific commit. This can be useful if, for example, you are new to a project, you want to quickly get the gist of a discussion, or you need to work on code that someone else wrote. However, it's important to note that {% data variables.product.prodname_copilot_chat_short %}'s summaries of releases, discussions, and commits may not always be accurate or complete.
{% data variables.product.prodname_copilot_chat_short %} can support a wide range of practical applications like Q&A, 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 {% data variables.product.prodname_copilot_chat_short %}, there are various measures that you can adopt. For more information on the limitations of {% data variables.product.prodname_copilot_chat_short %}, see "Limitations of {% data variables.product.prodname_copilot_chat %}."
{% data variables.product.prodname_copilot_chat_short %} 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.
While {% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot_chat_short %} to ensure that it meets your requirements and is free of errors or security concerns.
While {% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot_chat_short %}'s limitations.
{% data reusables.rai.copilot-dotcom-feedback-collection %}
If you encounter any issues or limitations with {% data variables.product.prodname_copilot_chat_dotcom_short %}, we recommend that you provide feedback by clicking the thumbs down icon below each chat response. This can help the developers to improve the tool and address any concerns or limitations.
{% data variables.product.prodname_copilot_chat_short %} is a new technology and is likely to evolve over time. For {% data variables.product.prodname_copilot_chat_dotcom %} you will always have access to the latest product experience. You should stay up to date with any new security risks or best practices that may emerge.
Depending on factors such as your codebase and input data, you may experience different levels of performance when using {% data variables.product.prodname_copilot_chat_short %}. The following information is designed to help you understand system limitations and key concepts about performance as they apply to {% data variables.product.prodname_copilot_chat_short %}.
{% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot %}'s best supported languages. Languages with less representation in public repositories may be more challenging for {% data variables.product.prodname_copilot_chat_short %} to provide assistance with. Additionally, {% data variables.product.prodname_copilot_chat_short %} 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.
{% data variables.product.prodname_copilot_short %}'s training data (drawn from existing code repositories) and context gathered by the large language model (for example, Bing search results) may contain biases and errors that can be perpetuated by the tool. Additionally, {% data variables.product.prodname_copilot_chat_short %} may be biased towards certain programming languages or coding styles, which can lead to suboptimal or incomplete code suggestions.
{% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot_chat_short %} to generate code for security-sensitive applications and always review and test the generated code thoroughly.
{% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot_chat_short %} suggestion may contain some code snippets that match code in the training set.
If you have disabled suggestions that match public code then {% data variables.product.prodname_copilot_chat_short %} utilizes filters that prevent it from showing code that matches code found in public repositories on {% data variables.product.prodname_dotcom %}. 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.
If you have enabled suggestions that match public code then {% data variables.product.prodname_copilot_chat_short %} displays a message if matching code is found. The message includes links to repositories on {% data variables.product.github %} that contain matching code, and any license details that were found. For more information, see "AUTOTITLE."
One of the limitations of {% data variables.product.prodname_copilot_chat_short %} 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.
{% data variables.product.prodname_copilot_chat_short %} 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 {% data variables.product.prodname_copilot_chat_short %} 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.
Depending on the question you ask, {% data variables.product.prodname_copilot_chat %} can optionally use a Bing search to help answer your question. {% data variables.product.prodname_copilot_short %} will use Bing for queries about recent events, new trends or technologies, highly specific subjects, or when a web search is explicitly requested by the user. Your {% data variables.product.prodname_enterprise %} administrator can enable Bing for your whole enterprise, or can delegate this decision to the organizational administrator. For more information, see "AUTOTITLE."
When leveraging Bing, {% data variables.product.prodname_copilot_short %} 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. {% data variables.product.prodname_copilot_short %} will provide a link to the search results with its response. The search query sent to Bing is governed by Microsoft's Privacy Statement.
For details of how to use {% data variables.product.prodname_copilot_chat_dotcom %}, see:
- "AUTOTITLE"{% ifversion fpt %} in the {% data variables.product.prodname_ghe_cloud %} documentation.{% endif %}