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title, shortTitle, intro, product, versions
| title | shortTitle | intro | product | versions | ||||
|---|---|---|---|---|---|---|---|---|
| About Copilot docset management | About docset management | {% data variables.product.prodname_copilot_for_docs %} can help you by providing a chat-based interface between you and specific docsets. You can ask questions within the scope of a given docset, and receive answers in synthesized summaries. | {% data reusables.gated-features.copilot-enterprise-beta %} |
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About {% data variables.product.prodname_copilot_for_docs %}
{% data variables.product.prodname_copilot_for_docs %} provides a chat-like interface to technical documentation stored in {% data variables.product.prodname_dotcom %} repositories. {% data variables.product.prodname_copilot_for_docs %} creates an index of a project's documentation. When you enter a query, {% data variables.product.prodname_copilot_short %} searches for relevant documentation snippets, synthesizes a summary of the relevant snippets to answer your question, and provides links to the source documentation for additional context.
{% data variables.product.prodname_copilot_for_docs %} uses a combination of natural language processing and machine learning to parse your question and provide you with an answer from the project’s technical documentation. This process can be broken down into a number of steps.
Input processing
The input prompt from the user is processed by {% data variables.product.prodname_copilot_short %} and sent to a large language model to get a response based on the context and prompt. User input mostly takes the form of natural language prompts or questions, but it could also be code snippets.
Language model analysis
The prompt is then passed through Azure's GPT LLM, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt and compares it to text data, which is available from {% data variables.product.prodname_dotcom %} repositories containing a project’s documentation, based on embeddings that are pre-generated for indexed repositories.
Response generation
The language model generates a response based on its analysis of the input prompt and comparison to existing repository embeddings. This response will take the form of a summary of the relevant information and links to the source documentation for further context.
Output formatting
The response generated by {% data variables.product.prodname_copilot_for_docs %} is formatted and presented to the user. {% data variables.product.prodname_copilot_for_docs %} may use syntax highlighting, indentation, and other formatting features to add clarity to the generated response. It will also provide you with direct links to the source contents.
{% data variables.product.prodname_copilot_for_docs %} 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_for_docs %} are responsible for reviewing and validating responses generated by the system to ensure they are accurate and appropriate. We encourage you to check the project’s technical documentation cited in the response to confirm the suitability of the AI-generated response. {% data variables.product.prodname_copilot_for_docs %} is also designed to learn from your feedback and improve over time.
Use case for {% data variables.product.prodname_copilot_for_docs %}
{% data variables.product.prodname_copilot_for_docs %} can help you find the answers you are looking for and present them to you succinctly. For example, {% data variables.product.prodname_copilot_for_docs %} can answer specific questions about using a particular library or framework, so that you don’t have to search the whole docset. {% data variables.product.prodname_copilot_for_docs %} will also reference existing documentation related to your questions so that you can find additional, contextual information.
{% data variables.product.prodname_copilot_for_docs %} is only intended to answer questions within the scope of a project’s existing documentation set.
Improving performance of {% data variables.product.prodname_copilot_for_docs %}
Use {% data variables.product.prodname_copilot_for_docs %} as a tool, not a replacement
While {% data variables.product.prodname_copilot_for_docs %} can be a powerful tool for answering specific questions about a project, you should always take advantage of the links to source material provided to validate the answer provided.
Provide feedback
We are currently in a beta phase with this product. If you encounter any issues or limitations with {% data variables.product.prodname_copilot_for_docs %}, we recommend that you provide feedback through the share feedback buttons at the bottom of each {% data variables.product.prodname_copilot_for_docs %} response within the chat interface in your browser. This can help the developers improve the tool and address any concerns or limitations.
Limitations of {% data variables.product.prodname_copilot_for_docs %}
Depending on factors such as your input question and the available project technical documentation, you may experience different levels of performance when using {% data variables.product.prodname_copilot_for_docs %}. 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_for_docs %}. {% data variables.product.prodname_copilot_for_docs %} has been subject to RAI Red Teaming, and we will continue to monitor the efficacy and safety of the feature over time.
Limited scope
{% data variables.product.prodname_copilot_for_docs %} is only suitable for asking questions that are answerable across a project’s existing technical documentation. It should not be used for general chat. The quality of responses will also be impacted by the way a question is asked and any variables you set on the user interface.
Potential biases and errors
{% data variables.product.prodname_copilot_for_docs %} data draws from existing project technical documentation, which may contain biases and errors of individuals who created the documentation, that can be perpetuated by the tool.
Inaccurate responses
One of the limitations of {% data variables.product.prodname_copilot_for_docs %} is that it may generate a response that appears to be valid but may not actually be semantically or syntactically correct or may not answer your question. To mitigate the impact of an inaccurate response, you should review the source documents referenced in the response when dealing with critical or sensitive applications. You should also ensure that any generated code blocks adhere to best practices and design patterns and fit within the overall architecture and style of your project.
Inaccurate responses
{% data variables.product.prodname_copilot_for_docs %} is not designed to answer questions beyond the scope of the existing project technical documentation. If a user asks {% data variables.product.prodname_copilot_for_docs %} a question that cannot be answered by existing project technical documentation, it may generate an answer that is irrelevant or nonsensical, or it may simply indicate that it is unable to provide a useful response.
Differing performance based on natural language
{% data variables.product.prodname_copilot_for_docs %} has been trained on natural language content written predominantly in English. As a result, you may notice differing performance when providing {% data variables.product.prodname_copilot_for_docs %} with natural language input prompts in languages other than English.