Merge pull request #49350 from github/timrogers/ce-skills-adaptations

Update Copilot Dotcom Chat docs to reflect Bing and symbol search skills

content/copilot/github-copilot-enterprise/copilot-chat-in-github/about-github-copilot-chat-in-githubcom.md

@@ -38,7 +38,9 @@ The chat interface provides access to coding information and support without req
 
 ### Input processing
 
-The input prompt from the user is pre-processed by the {% data variables.product.prodname_copilot_chat_short %} system and sent to a large language model to get a response based on the context and prompt. User input can take the form of code snippets or plain language. The system is only intended to respond to coding-related questions.
+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.
 
 ### Language model analysis
 
@@ -46,13 +48,13 @@ The pre-processed prompt is then passed through the {% data variables.product.pr
 
 ### Response generation
 
-The language model generates a response based on its analysis of the input prompt and the context provided to it. This response can take the form of generated code, code suggestions, or explanations of existing code.
+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.
 
 ### Output formatting
 
-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 or documentation, may also be provided.
+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, 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. {% data variables.product.prodname_copilot_chat_short %} is also designed to learn from your feedback and improve over time. 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 %}](#improving-performance-for-copilot-chat)."
+{% 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 %}](#improving-performance-for-copilot-chat)."
 
 ## Use cases for {% data variables.product.prodname_copilot_chat_short %}
 
@@ -62,7 +64,7 @@ The response generated by {% data variables.product.prodname_copilot_chat_short
 
 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, code in your repositories and Markdown documentation in your knowledge bases to answer your questions.
+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.
 
@@ -114,7 +116,7 @@ Depending on factors such as your codebase and input data, you may experience di
 
 ### Potential biases
 
-{% data variables.product.prodname_copilot_short %}'s training data is drawn from existing code repositories, which 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_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.
 
 ### Security risks
 

content/copilot/github-copilot-enterprise/copilot-chat-in-github/using-github-copilot-chat-in-githubcom.md

@@ -104,6 +104,7 @@ You can choose a particular repository, file or symbol, and then ask a question
    - What is the main purpose of this repo? What problem does it solve or what functionality does it provide?
    - What web frameworks are used in this project?
    - Where is rate limiting implemented in our API?
+   - How does the WidgetFactory class work?
    - How is the code organized? Explain the project architecture.
    - Are there any specific environment requirements for working on this project?