sdk

Author: eric-urban

articles/ai-studio/concepts/safety-evaluations-transparency-note.md

@@ -41,7 +41,7 @@ The Azure AI Studio safety evaluations let users evaluate the output of their ge
 
 ### System behavior
 
-Azure AI Studio provisions an Azure Open AI GPT-4 model and orchestrates adversarial attacks against your application to generate a high quality test dataset. It then provisions another GPT-4 model to annotate your test dataset for content and security. Users provide their generative AI application endpoint that they wish to test, and the safety evaluations will output a static test dataset against that endpoint along with its content risk label (Very low, Low, Medium, High) and reasoning for the AI-generated label.
+Azure AI Studio provisions an Azure OpenAI GPT-4 model and orchestrates adversarial attacks against your application to generate a high quality test dataset. It then provisions another GPT-4 model to annotate your test dataset for content and security. Users provide their generative AI application endpoint that they wish to test, and the safety evaluations will output a static test dataset against that endpoint along with its content risk label (Very low, Low, Medium, High) and reasoning for the AI-generated label.
 
 ### Use cases
 

articles/ai-studio/how-to/evaluate-flow-results.md

@@ -17,7 +17,7 @@ author: lgayhardt
 
 [!INCLUDE [Feature preview](../includes/feature-preview.md)]
 
-The Azure AI Studio evaluation page is a versatile hub that not only allows you to visualize and assess your results but also serves as a control center for optimizing, troubleshooting, and selecting the ideal AI model for your deployment needs. It's a one-stop solution for data-driven decision-making and performance enhancement in your AI Studio projects. You can seamlessly access and interpret the results from various sources, including your flow, the playground quick test session, evaluation submission UI, generative SDK, and CLI. This flexibility ensures that you can interact with your results in a way that best suits your workflow and preferences.
+The Azure AI Studio evaluation page is a versatile hub that not only allows you to visualize and assess your results but also serves as a control center for optimizing, troubleshooting, and selecting the ideal AI model for your deployment needs. It's a one-stop solution for data-driven decision-making and performance enhancement in your AI Studio projects. You can seamlessly access and interpret the results from various sources, including your flow, the playground quick test session, evaluation submission UI, and SDK. This flexibility ensures that you can interact with your results in a way that best suits your workflow and preferences.
 
 Once you've visualized your evaluation results, you can dive into a thorough examination. This includes the ability to not only view individual results but also to compare these results across multiple evaluation runs. By doing so, you can identify trends, patterns, and discrepancies, gaining invaluable insights into the performance of your AI system under various conditions.