Merge pull request #50988 from ShawnKupfer/WB1829

User Feedback 442346 - Fix incorrect bullet format per UUF feedback

Author: Stacyrch140

learn-pr/github/introduction-prompt-engineering-with-github-copilot/includes/1-introduction.md

@@ -3,10 +3,11 @@ GitHub Copilot, powered by OpenAI, is changing the game in software development.
 But to get the most out of GitHub Copilot, you need to know about prompt engineering. Prompt engineering is how you tell GitHub Copilot what you need. The quality of the code it gives back depends on how clear and accurate your prompts are.
 
 In this module, you'll learn about:
-Prompt engineering principles, best practices, and how GitHub Copilot learns from your prompts to provide context-aware responses.
-The underlying flow of how GitHub Copilot processes user prompts to generate responses or code suggestions.
-The data flow for code suggestions and chat in GitHub Copilot.
-LLMs (Large Language Models) and their role in GitHub Copilot and prompting.
-How to craft effective prompts that optimize GitHub Copilot's performance, ensuring precision and relevance in every code suggestion.
-The intricate relationship between prompts and Copilot's responses.
-How Copilot handles data from prompts in different situations, including secure transmission and content filtering.
+
+- Prompt engineering principles, best practices, and how GitHub Copilot learns from your prompts to provide context-aware responses.
+- The underlying flow of how GitHub Copilot processes user prompts to generate responses or code suggestions.
+- The data flow for code suggestions and chat in GitHub Copilot.
+- LLMs (Large Language Models) and their role in GitHub Copilot and prompting.
+- How to craft effective prompts that optimize GitHub Copilot's performance, ensuring precision and relevance in every code suggestion.
+- The intricate relationship between prompts and Copilot's responses.
+- How Copilot handles data from prompts in different situations, including secure transmission and content filtering.