use the new folder for AI foundry docs

Author: eric-urban

articles/ai-services/agents/how-to/virtual-networks.md

@@ -36,7 +36,7 @@ Azure AI Agent Service offers a standard agent configuration with private networ
 
 1. An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services).
 2. [Python 3.8 or later](https://www.python.org/)
-3. Ensure that the individual deploying the template has the [Azure AI Developer role](/azure/ai-foundry/concepts/rbac-ai-studio) assigned at the resource group level where the template is being deployed.
+3. Ensure that the individual deploying the template has the [Azure AI Developer role](/azure/ai-foundry/concepts/rbac-ai-foundry) assigned at the resource group level where the template is being deployed.
 4. Additionally, to deploy the template, you need to have the preset [Role Based Access Administrator](/azure/role-based-access-control/built-in-roles/privileged#role-based-access-control-administrator) role at the subscription level.
     * The **Owner** role at the subscription level satisfies this requirement.
     * The specific admin role that is needed is `Microsoft.Authorization/roleAssignments/write`

articles/ai-services/content-safety/how-to/improve-performance.md

@@ -37,7 +37,7 @@ If your assessment confirms that you found a false positive or false negative, y
 
 #### [Content Safety standalone API](#tab/standalone-api)
 
-If you're using the Azure AI Content Safety standalone API directly, try experimenting by setting the severity threshold at different levels for [harm categories](/azure/ai-services/content-safety/concepts/harm-categories?tabs=definitions) based on API output. Alternatively, if you prefer the no-code approach, you can try out those settings in [Content Safety Studio](https://contentsafety.cognitive.azure.com/) or Azure AI Foundry’s [Content Safety page](https://ai.azure.com/explore/contentsafety). Instructions can be found [here](/azure/ai-foundry/quickstarts/content-safety?tabs=moderate-text-content). 
+If you're using the Azure AI Content Safety standalone API directly, try experimenting by setting the severity threshold at different levels for [harm categories](/azure/ai-services/content-safety/concepts/harm-categories?tabs=definitions) based on API output. Alternatively, if you prefer the no-code approach, you can try out those settings in [Content Safety Studio](https://contentsafety.cognitive.azure.com/) or Azure AI Foundry’s [Content Safety page](https://ai.azure.com/explore/contentsafety). Instructions can be found [here](/azure/ai-foundry/concepts/content-filtering). 
 
 In addition to adjusting the severity levels for false negatives, you can also use blocklists. More information on using blocklists for text moderation can be found in [Use blocklists for text moderation](/azure/ai-services/content-safety/how-to/use-blocklist?tabs=windows%2Crest).
 

articles/ai-services/includes/quickstarts/ai-studio-prerequisites.md renamed to articles/ai-services/includes/quickstarts/ai-foundry-prerequisites.md

@@ -17,7 +17,7 @@ ms.custom: language-service-pii
 Azure AI Language Personally Identifiable Information (PII) detection is a feature offered by [Azure AI Language](../overview.md). The PII detection service is a cloud-based API that utilizes machine learning and AI algorithms to help you develop intelligent applications with advanced natural language understanding. Azure AI Language PII detection uses Named Entity Recognition (NER) to **identify and redact** sensitive information from input data. The service classifies sensitive personal data into predefined categories. These categories include phone numbers, email addresses, and identification documents. This classification helps to efficiently detect and eliminate such information.
 
 > [!TIP]
-> Try PII detection [in Azure AI Foundry portal](https://ai.azure.com/explore/language). There you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-foundry/ai-services/connect-ai-services.md).
+> Try PII detection [in Azure AI Foundry portal](https://ai.azure.com/explore/language). There you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-foundry/ai-services/how-to/connect-ai-services.md).
 
 ## What's new
 

articles/ai-services/language-service/personally-identifiable-information/overview.md

@@ -22,7 +22,7 @@ Use this article to learn more about this feature, and how to use it in your app
 Out of the box, the service provides summarization solutions for three types of genre, plain texts, conversations, and native documents. Text summarization only accepts plain text blocks. Conversation summarization accepts conversational input, including various speech audio signals. Native document summarization accepts documents in their native formats, such as Word, PDF, or plain text. For more information, *see* [Supported document formats](../native-document-support/overview.md#supported-document-formats).
 
 > [!TIP]
-> Try out Summarization [in Azure AI Foundry portal](https://ai.azure.com/explore/language). There you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-foundry/ai-services/connect-ai-services.md) in order to use this service.
+> Try out Summarization [in Azure AI Foundry portal](https://ai.azure.com/explore/language). There you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-foundry/ai-services/how-to/connect-ai-services.md) in order to use this service.
 
 ## Capabilities
 

articles/ai-services/language-service/summarization/overview.md

@@ -19,7 +19,7 @@ ms.custom: language-service-health, ignite-2024
 Text Analytics for health is one of the prebuilt features offered by [Azure AI Language](../overview.md). It is a cloud-based API service that applies machine-learning intelligence to extract and label relevant medical information from a variety of unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records. 
 
 > [!TIP]
-> Try out Text Analytics for health [in Azure AI Foundry portal](https://ai.azure.com/explore/language), where you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-foundry/ai-services/connect-ai-services.md) in order to use this service. 
+> Try out Text Analytics for health [in Azure AI Foundry portal](https://ai.azure.com/explore/language), where you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-foundry/ai-services/how-to/connect-ai-services.md) in order to use this service. 
 
 This documentation contains the following types of articles:
 * The [**quickstart article**](quickstart.md) provides a short tutorial that guides you with making your first request to the service.