formatting fix

Author: PatrickFarley

articles/ai-services/openai/concepts/content-filter.md

@@ -58,7 +58,7 @@ The default content filtering configuration is set to filter at the medium sever
 | High              | If approved<sup>\*</sup>| If approved<sup>\*</sup> | Content detected at severity levels low and medium isn't filtered. Only content at severity level high is filtered. Requires approval<sup>\*</sup>.|
 | No filters | If approved<sup>\*</sup>| If approved<sup>\*</sup>| No content is filtered regardless of severity level detected. Requires approval<sup>\*</sup>.|
 
-<sup>\*</sup> Only customers who have been approved for modified content filtering  have full content filtering control, including configuring content filters at severity level high only or turning content filters off. Apply for modified content filters via this form: [Azure OpenAI Limited Access Review:  Modified Content Filtering (microsoft.com)](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUMlBQNkZMR0lFRldORTdVQzQ0TEI5Q1ExOSQlQCN0PWcu)
+<sup>\*</sup> Only customers who have been approved for modified content filtering  have full content filtering control, including configuring content filters at severity level high only or turning content filters off. Apply for modified content filters via this form: [Azure OpenAI Limited Access Review:  Modified Content Filtering (microsoft.com)](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUMlBQNkZMR0lFRldORTdVQzQ0TEI5Q1ExOSQlQCN0PWcu)
 
 Content filtering configurations are created within a Resource in Azure AI Studio, and can be associated with Deployments. [Learn more about configurability here](../how-to/content-filters.md).  
 

articles/ai-services/openai/concepts/use-your-data.md

@@ -288,7 +288,7 @@ Use the following sections to help you configure Azure OpenAI on your data for o
 
 ### System message
 
-Give the model instructions about how it should behave and any context it should reference when generating a response. You can describe the assistant's personality, what it should and shouldn't answer, and how to format responses. There's no token limit for the system message, but will be included with every API call and counted against the overall token limit. The system message will be truncated if it's greater than 400 tokens. 
+Give the model instructions about how it should behave and any context it should reference when generating a response. You can describe the assistant's personality, what it should and shouldn't answer, and how to format responses. There's no token limit for the system message, but will be included with every API call and counted against the overall token limit. The system message will be truncated if it's greater than 400 tokens. 
 
 For example, if you're creating a chatbot where the data consists of transcriptions of quarterly financial earnings calls, you might use the following system message:
 

articles/ai-services/openai/includes/dall-e-rest.md

@@ -71,16 +71,16 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
     api_base = '<your_endpoint>'  # Enter your endpoint here
     api_key = '<your_key>'        # Enter your API key here
 
-    api_version = '2023-11-01-preview'
+    api_version = '2023-12-01-preview'
     url = f"{api_base}/openai/deployments/<dalle3>/images/generations?api-version={api_version}"
     headers= { "api-key": api_key, "Content-Type": "application/json" }
     body = {
         # Enter your prompt text here
         "prompt": "A multi-colored umbrella on the beach, disposable camera",
         "size": "1024x1024", # supported values are “1792x1024”, “1024x1024” and “1024x1792” 
         "n": 1,
-        "quality": "hd", # Options are “hd” and “standard”; defaults to standard 
-        "style": "vivid" # Options are “natural” and “vivid”; defaults to “vivid”
+        "quality": "hd", # Options are “hd” and “standard”; defaults to standard 
+        "style": "vivid" # Options are “natural” and “vivid”; defaults to “vivid”
     }
     submission = requests.post(url, headers=headers, json=body)
 
@@ -151,13 +151,13 @@ The output from a successful image generation API call looks like the following
 
 ```json
 { 
-    "created": 1698116662, 
-    "data": [ 
-        { 
-            "url": "<URL_to_generated_image>",
-            "revised_prompt": "<prompt_that_was_used>" 
-        }
-    ]
+    "created": 1698116662, 
+    "data": [ 
+        { 
+            "url": "<URL_to_generated_image>",
+            "revised_prompt": "<prompt_that_was_used>" 
+        }
+    ]
 } 
 ```
 

articles/ai-services/openai/reference.md

@@ -553,8 +553,8 @@ curl -X POST https://{your-resource-name}.openai.azure.com/openai/{deployment-id
     "prompt": "An avocado chair",
     "size": "1024x1024",
     "n": 3,
-    "quality": "hd", 
-    "style": "vivid"
+    "quality": "hd", 
+    "style": "vivid"
   }'
 ```
 
@@ -564,17 +564,17 @@ The operation returns a `202` status code and an `GenerateImagesResponse` JSON o
 
 ```json
 { 
-    "created": 1698116662, 
-    "data": [ 
+    "created": 1698116662, 
+    "data": [ 
         { 
-            "url": "url to the image", 
-            "revised_prompt": "the actual prompt that was used" 
+            "url": "url to the image", 
+            "revised_prompt": "the actual prompt that was used" 
         }, 
         { 
-            "url": "url to the image" 
-        },
+            "url": "url to the image" 
+        },
         ...
-    ]
+    ]
 } 
 ```