pr fix

Author: PatrickFarley

articles/ai-studio/ai-services/how-to/content-safety.md

@@ -25,9 +25,9 @@ Azure AI Studio includes a Content Safety **try it out** page that lets you use
 Follow these steps to use the Content Safety **try it out** page: 
 
 1. Go to [AI Studio](https://ai.azure.com/) and navigate to your project/hub. Then select the **Safety+ Security** tab on the left nav and select the **Try it out** tab.
-    tbd image
 1. On the **Try it out** page, you can experiment with various content safety features such as text and image content, using adjustable thresholds to filter for inappropriate or harmful content.
-    :::image type="content" source="../../media/content-safety/try-it-out.png" alt-text="Screenshot of the try it out page for content safety.":::
+
+:::image type="content" source="../../media/content-safety/try-it-out.png" alt-text="Screenshot of the try it out page for content safety.":::
 
 ## Analyze text
 

articles/ai-studio/responsible-use-of-ai-overview.md

@@ -76,8 +76,6 @@ Mitigating harms presented by large language models such as the Azure OpenAI mod
 
 :::image type="content" source="media/content-safety/mitigation-layers.png" alt-text="Diagram of mitigation layers":::
 
-TBD
-
 ### System message and grounding layer 
 
 System message (otherwise known as metaprompt) design and proper data grounding are at the heart of every generative AI application. They provide an application's unique differentiation and are also a key component in reducing errors and mitigating risks. At Microsoft, we find [retrieval augmented generation (RAG)](/azure/ai-studio/concepts/retrieval-augmented-generation) to be an effective and flexible architecture. With RAG, you enable your application to retrieve relevant knowledge from selected data and incorporate it into your system message to the model. In this pattern, rather than using the model to store information, which can change over time and based on context, the model functions as a reasoning engine over the data provided to it during the query. This improves the freshness, accuracy, and relevancy of inputs and outputs. In other words, RAG can ground your model in relevant data for more relevant results.