Merge branch 'main' of https://github.com/MicrosoftDocs/azure-ai-docs-pr into evalairedteaming0525

Author: lgayhardt

.openpublishing.redirection.json

@@ -309,6 +309,16 @@
       "source_path_from_root": "/articles/ai-services/speech-service/how-to-migrate-to-prebuilt-neural-voice.md",
       "redirect_url": "/azure/ai-services/speech-service/custom-neural-voice",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-foundry/quickstarts/hear-speak-playground.md",
+      "redirect_url": "/azure/ai-foundry/quickstarts/get-started-playground",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/tutorials/prompt-flow.md",
+      "redirect_url": "/azure/ai-services/language-service/tutorials/power-automate",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-foundry/ai-services/content-safety-overview.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2024
 ms.topic: overview
-ms.date: 05/01/2025
+ms.date: 05/31/2025
 ms.author: pafarley
 author: PatrickFarley
 ---

articles/ai-foundry/concepts/ai-resources.md

@@ -2,18 +2,20 @@
 title: Hubs and hub-based project overview
 titleSuffix: Azure AI Foundry
 description: This article introduces concepts about Azure AI Foundry hubs for your Azure AI Foundry projects.
+author: Blackmist
+ms.author: larryfr
 manager: scottpolly
+ms.reviewer: deeikele
+ms.date: 04/28/2025
 ms.service: azure-ai-foundry
+ms.topic: conceptual
 ms.custom:
   - ignite-2023
   - build-2024
   - ai-learning-hub
   - ignite-2024
-ms.topic: conceptual
-ms.date: 04/28/2025
-ms.reviewer: deeikele
-ms.author: larryfr
-author: Blackmist
+  - build-aifnd
+  - build-2025
 ---
 
 # Hub resources overview

articles/ai-foundry/concepts/content-filtering.md

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: conceptual
-ms.date: 04/29/2025
+ms.date: 05/31/2025
 ms.reviewer: eur
 ms.author: pafarley
 author: PatrickFarley
@@ -24,9 +24,9 @@ author: PatrickFarley
 
 ## How it works 
 
-The content filtering system is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md), and it works by running both the prompt input and completion output through a set of classification models designed to detect and prevent the output of harmful content. Variations in API configurations and application design might affect completions and thus filtering behavior.
+The content filtering system is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md), and it works by running both the model prompt input and completion output through a set of classification models designed to detect and prevent the output of harmful content. Variations in API configurations and application design might affect completions and thus filtering behavior.
 
-With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later on). Models available through **standard deployments** have content filtering enabled by default. To learn more about the default content filter enabled for standard deployments, see [Content safety for Models Sold Directly by Azure ](model-catalog-content-safety.md).
+With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later). Models available through **standard deployments** have content filtering enabled by default. To learn more about the default content filter enabled for standard deployments, see [Content safety for Models Sold Directly by Azure ](model-catalog-content-safety.md).
 
 ## Language support
 
@@ -73,20 +73,7 @@ You can also enable the following special output filters:
 
 ### Configurability (preview)
 
-The default content filtering configuration for the GPT model series is set to filter at the medium severity threshold for all four content harm categories (hate, violence, sexual, and self-harm) and applies to both prompts (text, multi-modal text/image) and completions (text). This means that content that is detected at severity level medium or high is filtered, while content detected at severity level low isn't filtered by the content filters. For DALL-E, the default severity threshold is set to low for both prompts (text) and completions (images), so content detected at severity levels low, medium, or high is filtered. 
-
-The configurability feature allows customers to adjust the settings, separately for prompts and completions, to filter content for each content category at different severity levels as described in the table below:
-
-| Severity filtered | Configurable for prompts | Configurable for completions | Descriptions |
-|-------------------|--------------------------|------------------------------|--------------|
-| Low, medium, high | Yes | Yes | Strictest filtering configuration. Content detected at severity levels low, medium and high is filtered.|
-| Medium, high      | Yes | Yes | Content detected at severity level low isn't filtered, content at medium and high is filtered.|
-| High              | Yes| Yes | Content detected at severity levels low and medium isn't filtered. Only content at severity level high is filtered. Requires approval<sup>1</sup>.|
-| No filters | If approved<sup>1</sup>| If approved<sup>1</sup>| No content is filtered regardless of severity level detected. Requires approval<sup>1</sup>.|
-
-<sup>1</sup> For Azure OpenAI models, 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 off content filters. Apply for modified content filters via these forms: [Azure OpenAI Limited Access Review: Modified Content Filters](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUMlBQNkZMR0lFRldORTdVQzQ0TEI5Q1ExOSQlQCN0PWcu), and [Modified Abuse Monitoring](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUOE9MUTFMUlpBNk5IQlZWWkcyUEpWWEhGOCQlQCN0PWcu).
-
-Customers are responsible for ensuring that applications integrating Azure OpenAI comply with the [Code of Conduct](/legal/ai-code-of-conduct?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext). 
+[!INCLUDE [content-filter-configurability](../../ai-services/openai/includes/content-filter-configurability.md)]
 
 
 ## Related content

articles/ai-foundry/concepts/encryption-keys-portal.md

@@ -4,12 +4,14 @@ titleSuffix: Azure AI Foundry
 description: Learn about using customer-managed keys for encryption to improve data security with Azure AI Foundry.
 author: Blackmist
 ms.author: larryfr
+ms.reviewer: deeikele
+ms.date: 05/01/2025
 ms.service: azure-ai-services
+ms.topic: concept-article
 ms.custom:
   - ignite-2023
-ms.topic: concept-article
-ms.date: 05/01/2025
-ms.reviewer: deeikele
+  - build-aifnd
+  - build-2025
 zone_pivot_groups: project-type
 # Customer intent: As an admin, I want to understand how I can use my own encryption keys with Azure AI Foundry.
 ---

articles/ai-foundry/concepts/evaluation-evaluators/agent-evaluators.md

@@ -2,13 +2,16 @@
 title: Agent evaluators for generative AI
 titleSuffix: Azure AI Foundry
 description: Learn how to evaluate Azure AI agents using intent resolution, tool call accuracy, and task adherence evaluators.
+author: lgayhardt
+ms.author: lagayhar
 manager: scottpolly
+ms.reviewer: changliu2
+ms.date: 05/19/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
-ms.date: 05/19/2025
-ms.reviewer: changliu2
-ms.author: lagayhar
-author: lgayhardt
+ms.custom:
+  - build-aifnd
+  - build-2025
 ---
 
 # Agent evaluators (preview)

articles/ai-foundry/concepts/evaluation-evaluators/azure-openai-graders.md

@@ -2,13 +2,16 @@
 title: Azure OpenAI Graders for generative AI
 titleSuffix: Azure AI Foundry
 description: Learn about Azure OpenAI Graders for evaluating AI model outputs, including label grading, string checking, text similarity, and custom grading.
+author: lgayhardt
+ms.author: lagayhar
 manager: scottpolly
+ms.reviewer: mithigpe
+ms.date: 05/19/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
-ms.date: 05/19/2025
-ms.reviewer: mithigpe
-ms.author: lagayhar
-author: lgayhardt
+ms.custom:
+  - build-aifnd
+  - build-2025
 ---
 
 # Azure OpenAI Graders (preview)

articles/ai-foundry/concepts/evaluation-evaluators/custom-evaluators.md

@@ -2,13 +2,16 @@
 title: Custom evaluators
 titleSuffix: Azure AI Foundry
 description: Learn how to create custom evaluators for your AI applications using code-based or prompt-based approaches.
+author: lgayhardt
+ms.author: lagayhar
 manager: scottpolly
+ms.reviewer: mithigpe
+ms.date: 05/19/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
-ms.date: 05/19/2025
-ms.reviewer: mithigpe
-ms.author: lagayhar
-author: lgayhardt
+ms.custom:
+  - build-aifnd
+  - build-2025
 ---
 
 # Custom evaluators
@@ -152,4 +155,4 @@ friendliness_score = friendliness_eval(response="I will not apologize for my beh
 ## Related content
 
 - [How to run batch evaluation on a dataset](../../how-to/develop/evaluate-sdk.md#local-evaluation-on-test-datasets-using-evaluate)  
-- [How to run batch evaluation on a target](../../how-to/develop/evaluate-sdk.md#local-evaluation-on-a-target)
+- [How to run batch evaluation on a target](../../how-to/develop/evaluate-sdk.md#local-evaluation-on-a-target)

articles/ai-foundry/concepts/evaluation-evaluators/general-purpose-evaluators.md

@@ -2,13 +2,16 @@
 title: General purpose evaluators for generative AI
 titleSuffix: Azure AI Foundry
 description: Learn about general-purpose evaluators for generative AI, including coherence, fluency, and question-answering composite evaluation.
+author: lgayhardt
+ms.author: lagayhar
 manager: scottpolly
+ms.reviewer: changliu2
+ms.date: 05/19/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
-ms.date: 05/19/2025
-ms.reviewer: changliu2
-ms.author: lagayhar
-author: lgayhardt
+ms.custom:
+  - build-aifnd
+  - build-2025
 ---
 
 # General purpose evaluators
@@ -161,4 +164,4 @@ While F1 score outputs a numerical score on 0-1 float scale, the other evaluator
 ## Related content
 
 - [How to run batch evaluation on a dataset](../../how-to/develop/evaluate-sdk.md#local-evaluation-on-test-datasets-using-evaluate)  
-- [How to run batch evaluation on a target](../../how-to/develop/evaluate-sdk.md#local-evaluation-on-a-target)
+- [How to run batch evaluation on a target](../../how-to/develop/evaluate-sdk.md#local-evaluation-on-a-target)

articles/ai-foundry/concepts/evaluation-evaluators/rag-evaluators.md

@@ -2,13 +2,16 @@
 title: Retrieval-augmented Generation (RAG) evaluators for generative AI
 titleSuffix: Azure AI Foundry
 description: Learn about Retrieval-augmented Generation (RAG) evaluators for assessing relevance, groundedness, and response completeness in generative AI systems.
+author: lgayhardt
+ms.author: lagayhar
 manager: scottpolly
+ms.reviewer: changliu2
+ms.date: 05/19/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
-ms.date: 05/19/2025
-ms.reviewer: changliu2
-ms.author: lagayhar
-author: lgayhardt
+ms.custom:
+  - build-aifnd
+  - build-2025
 ---
 
 # Retrieval-augmented Generation (RAG) evaluators