address PR review comments

Author: mrbullwinkle

articles/cognitive-services/openai/concepts/system-message.md

@@ -28,13 +28,13 @@ The LLM system message framework described here covers four concepts:
 
 ## Define the model’s profile, capabilities, and limitations for your scenario
 
-1. **Define the specific task(s)** you would like the model to complete. Describe who the users of the model will be, what inputs they will provide to the model, and what you expect the model to do with the inputs.
+- **Define the specific task(s)** you would like the model to complete. Describe who the users of the model will be, what inputs they will provide to the model, and what you expect the model to do with the inputs.
 
-2. **Define how the model should complete the tasks**, including any additional tools (like APIs, code, plug-ins) the model can use. If it doesn’t use additional tools, it can rely on its own parametric knowledge.
+- **Define how the model should complete the tasks**, including any additional tools (like APIs, code, plug-ins) the model can use. If it doesn’t use additional tools, it can rely on its own parametric knowledge.
 
-3. **Define the scope and limitations** of the model’s performance. Provide clear instructions on how the model should respond when faced with any limitations. For example, define how the model should respond if prompted on subjects or for uses that are off topic or otherwise outside of what you want the system to do.
+- **Define the scope and limitations** of the model’s performance. Provide clear instructions on how the model should respond when faced with any limitations. For example, define how the model should respond if prompted on subjects or for uses that are off topic or otherwise outside of what you want the system to do.
 
-4. **Define the posture and tone** the model should exhibit in its responses.
+- **Define the posture and tone** the model should exhibit in its responses.
 
 Here are some examples of lines you can include:
 
@@ -52,9 +52,9 @@ Here are some examples of lines you can include:
 
 When using the system message to define the model’s desired output format in your scenario, consider and include the following types of information:
 
-1. **Define the language and syntax** of the output format. If you want the output to be machine parse-able, you may want the output to be in formats like JSON, XSON or XML.
+- **Define the language and syntax** of the output format. If you want the output to be machine parse-able, you may want the output to be in formats like JSON, XSON or XML.
 
-2. **Define any styling or formatting** preferences for better user or machine readability. For example, you may want relevant parts of the response to be bolded or citations to be in a specific format.
+- **Define any styling or formatting** preferences for better user or machine readability. For example, you may want relevant parts of the response to be bolded or citations to be in a specific format.
 
 Here are some examples of lines you can include:
 
@@ -69,8 +69,8 @@ Here are some examples of lines you can include:
 
 When using the system message to demonstrate the intended behavior of the model in your scenario, it is helpful to provide specific examples. When providing examples, consider the following:
 
-1. Describe difficult use cases where the prompt is ambiguous or complicated, to give the model additional visibility into how to approach such cases.
-2. Show the potential “inner monologue” and chain-of-thought reasoning to better inform the model on the steps it should take to achieve the desired outcomes.
+- Describe difficult use cases where the prompt is ambiguous or complicated, to give the model additional visibility into how to approach such cases.
+- Show the potential “inner monologue” and chain-of-thought reasoning to better inform the model on the steps it should take to achieve the desired outcomes.
 
 Here is an example: