address PR review issues

eric-urban · eric-urban · commit 1f5de5543a07 · 2024-05-19T06:39:56.000-07:00
diff --git a/articles/ai-studio/concepts/content-filtering.md b/articles/ai-studio/concepts/content-filtering.md
@@ -50,7 +50,7 @@ Follow these steps to create a content filter:
 
 1. On the **Output filters** page, you can set the filter for the output completion. For example, you can enable filters for protected material detection. Then select **Next**. 
     
-    Content will be annotated by each categories and blocked according to the threshold. For violent content, hate content, sexual content, and self-harm content category, adjust the threshold to block harmful content with equal or higher severity levels.
+    Content will be annotated by each category and blocked according to the threshold. For violent content, hate content, sexual content, and self-harm content category, adjust the threshold to block harmful content with equal or higher severity levels.
 
 1. Optionally, on the **Deployment** page, you can associate the content filter with a deployment. You can also associate the content filter with a deployment later. Then select **Create**.
 
diff --git a/articles/ai-studio/how-to/create-manage-compute.md b/articles/ai-studio/how-to/create-manage-compute.md
@@ -50,7 +50,7 @@ To create a compute instance in Azure AI Studio:
     
     For more information on configuration details such as CPU and RAM, see [Azure Machine Learning pricing](https://azure.microsoft.com/pricing/details/machine-learning/) and [virtual machine sizes](/azure/virtual-machines/sizes).
 
-1. On the **Scheduling** page under **Auto shut down** make sure idle shutdown is enabled by default. You can opt to automatically shutdown compute after the instance has been idle for a set amount of time. If you disable auto shutdown costs continue to accrue even during periods of inactivity. For more information, see [Configure idle shutdown](#configure-idle-shutdown).
+1. On the **Scheduling** page under **Auto shut down** make sure idle shutdown is enabled by default. You can opt to automatically shut down compute after the instance has been idle for a set amount of time. If you disable auto shutdown costs continue to accrue even during periods of inactivity. For more information, see [Configure idle shutdown](#configure-idle-shutdown).
 
     :::image type="content" source="../media/compute/compute-scheduling.png" alt-text="Screenshot of the option to enable idle shutdown and create a schedule." lightbox="../media/compute/compute-scheduling.png":::
 
diff --git a/articles/ai-studio/how-to/evaluate-generative-ai-app.md b/articles/ai-studio/how-to/evaluate-generative-ai-app.md
@@ -165,7 +165,7 @@ To use the evaluator library in AI Studio, go to your project's **Evaluation** p
 :::image type="content" source="../media/evaluations/evaluate/evaluator-library-list.png" alt-text="Screenshot of the page to select evaluators from the evaluator library." lightbox="../media/evaluations/evaluate/evaluator-library-list.png":::
 
 You can select the evaluator name to see more details. You can see the name, description, and parameters, and check any files associated with the evaluator. Here are some examples of Microsoft curated evaluators:
-- For performance and quality evaluators curated by Microsoft, you can view the annotation prompt on the details page. You can adapt these prompts to your own use case by changing the parameters or criteria according to your data and objectives [with the prompt flow SDK](../how-to/develop/flow-evaluate-sdk.md#custom-evaluators). For example, you can select *Groundedness-Evaluator* and check the prompty file showing how we calculate the metric.
+- For performance and quality evaluators curated by Microsoft, you can view the annotation prompt on the details page. You can adapt these prompts to your own use case by changing the parameters or criteria according to your data and objectives [with the prompt flow SDK](../how-to/develop/flow-evaluate-sdk.md#custom-evaluators). For example, you can select *Groundedness-Evaluator* and check the Prompty file showing how we calculate the metric.
 - For risk and safety evaluators curated by Microsoft, you can see the definition of the metrics. For example, you can select the *Self-Harm-Related-Content-Evaluator* and learn what it means and how Microsoft determines the various severity levels for this safety metric
 
 
diff --git a/articles/ai-studio/whats-new.md b/articles/ai-studio/whats-new.md
@@ -98,7 +98,7 @@ Microsoft curated evaluators are also available in the AI Studio evaluator libra
 
 ### Use Prompty for engineering and sharing prompts 
 
-Prompty is a new prompt template part of the prompt flow SDK that can be run standalone and integrated into your code. You can download a prompty from the AI Studio playground, continue iterating on it in your local development environment, and check it into your git repo to share and collaborate on prompts with others. The prompty format is supported in Semantic Kernel, C#, and LangChain as a community extension.  
+Prompty is a new prompt template part of the prompt flow SDK that can be run standalone and integrated into your code. You can download a Prompty from the AI Studio playground, continue iterating on it in your local development environment, and check it into your git repo to share and collaborate on prompts with others. The Prompty format is supported in Semantic Kernel, C#, and LangChain as a community extension.  
 
 ### Mistral Small
 
@@ -118,7 +118,7 @@ For more information about Phi-3, see the [blog announcement](https://techcommun
 
 ### Phi-3
 
-The Phi-3 family of models developed by Microsoft are available in the Azure AI model catalog. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across various language, reasoning, coding, and math benchmarks. This release expands the selection of high-quality models for customers, offering more practical choices as they compose and build generative AI applications.
+The Phi-3 family of models developed by Microsoft is available in the Azure AI model catalog. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across various language, reasoning, coding, and math benchmarks. This release expands the selection of high-quality models for customers, offering more practical choices as they compose and build generative AI applications.
 
 - Phi-3-mini is available in two context-length variants—4K and 128K tokens. It's the first model in its class to support a context window of up to 128K tokens, with little effect on quality.
 - It's instruction-tuned, meaning that it's trained to follow different types of instructions reflecting how people normally communicate. This ensures the model is ready to use out-of-the-box.
