Merge pull request #5170 from aahill/language-freshness

freshness updates

Author: v-shils

articles/ai-services/language-service/concepts/data-limits.md

@@ -6,7 +6,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 04/29/2025
+ms.date: 05/23/2025
 ms.author: jboback
 ---
 

articles/ai-services/language-service/conversational-language-understanding/how-to/migrate-from-luis.md

@@ -6,7 +6,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 04/29/2025
+ms.date: 05/23/2025
 ms.author: jboback
 ms.custom: language-service-clu
 ---
@@ -22,7 +22,7 @@ CLU offers the following advantages over LUIS:
 - Ease of integration with different CLU and [custom question answering](../../question-answering/overview.md) projects using [orchestration workflow](../../orchestration-workflow/overview.md). 
 - The ability to add testing data within the experience using Language Studio and APIs for model performance evaluation prior to deployment. 
 
-To get started, you can [create a new project](../quickstart.md?pivots=language-studio#create-a-conversational-language-understanding-project) or [migrate your LUIS application](#migrate-your-luis-applications). 
+To get started, you can [use CLU directly](../quickstart.md) or [migrate your LUIS application](#migrate-your-luis-applications). 
 
 ## Comparison between LUIS and CLU
 
@@ -33,7 +33,7 @@ The following table presents a side-by-side comparison between the features of L
 |Machine-learned and Structured ML entities| Learned [entity components](#how-are-entities-different-in-clu) |Machine-learned entities without subentities are transferred as CLU entities. Structured ML entities only transfer leaf nodes (lowest level subentities that don't have their own subentities) as entities in CLU. The name of the entity in CLU is the name of the subentity concatenated with the parent. For example, _Order.Size_|
 |List, regex, and prebuilt entities| List, regex, and prebuilt [entity components](#how-are-entities-different-in-clu) | List, regex, and prebuilt entities are transferred as entities in CLU with a populated entity component based on the entity type.|
 |`Pattern.Any` entities| Not currently available | `Pattern.Any` entities are removed.|
-|Single culture for each application|[Multilingual models](#how-is-conversational-language-understanding-multilingual) enable multiple languages for each project. |The primary language of your project are set as your LUIS application culture. Your project can be trained to extend to different languages.|
+|Single culture for each application|[Multilingual models](#how-is-conversational-language-understanding-multilingual) enable multiple languages for each project. |The primary language of your project is set as your LUIS application culture. Your project can be trained to extend to different languages.|
 |Entity roles  |[Roles](#how-are-entity-roles-transferred-to-clu) are no longer needed. | Entity roles are transferred as entities.|
 |Settings for: normalize punctuation, normalize diacritics, normalize word form, use all training data  |[Settings](#how-is-the-accuracy-of-clu-better-than-luis) are no longer needed. |Settings aren't transferred.  |
 |Patterns and phrase list features|[Patterns and Phrase list features](#how-is-the-accuracy-of-clu-better-than-luis) are no longer needed. |Patterns and phrase list features aren't transferred.  |

articles/ai-services/language-service/conversational-language-understanding/includes/quickstarts/language-studio.md

@@ -3,7 +3,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: include
-ms.date: 11/21/2024
+ms.date: 05/23/2025
 ms.author: jboback
 ---
 
@@ -24,12 +24,6 @@ ms.author: jboback
 
     :::image type="content" source="../../../media/azure-portal-resource-credentials.png" alt-text="A screenshot showing the key and endpoint page in the Azure portal" lightbox="../../../media/azure-portal-resource-credentials.png":::
 
-## Import a new CLU fine-tuning project
-
-Once you have a Language resource or an Azure AI resource created, create a fine-tuning task and select Azure AI language and select Conversational language understanding as the task type. A task is a work area for building your custom models based on your data. Your task can only be accessed by you and others who have access to the resource being used. 
-
-For this quickstart, you can download [this sample](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/language-service/CLU/EmailAppDemo.json) and import it. This task can predict the intended commands from user input, such as: reading emails, deleting emails, and attaching a document to an email. 
-
 ## Import a new CLU sample project
 
 Once you have a Language resource created, create a conversational language understanding project. A project is a work area for building your custom ML models based on your data. Your project can only be accessed by you and others who have access to the Language resource being used.

articles/ai-services/language-service/conversational-language-understanding/includes/quickstarts/rest-api.md

@@ -46,7 +46,7 @@ In a large corporation, an enterprise chat bot may handle various employee affai
 
 ### Agents
 
-CLU is utilized by the [intent routing](https://aka.ms/intent-triage-agent-template) agent template, which detects user intent and provides exact answering. Perfect for deterministically intent routing and exact question answering with human control.
+CLU is utilized by the [intent routing](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/intent-routing-agent) agent template, which detects user intent and provides exact answering. Perfect for deterministically intent routing and exact question answering with human control.
 
 ## Project development lifecycle
 

articles/ai-services/language-service/conversational-language-understanding/overview.md

@@ -6,28 +6,17 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 05/01/2025
+ms.date: 05/23/2025
 ms.author: jboback
 ms.custom: language-service-clu, mode-other
-zone_pivot_groups: usage-custom-language-features-foundry
 ---
 
 # Quickstart: Conversational language understanding 
 
 Use this article to get started with Conversational Language understanding using Azure AI Foundry and the REST API. Follow these steps to try out an example.
 
-::: zone pivot="azure-ai-foundry"
-
-[!INCLUDE [Language Studio quickstart](includes/quickstarts/language-studio.md)]
-
-::: zone-end
-
-::: zone pivot="rest-api"
-
 [!INCLUDE [REST API quickstart](includes/quickstarts/rest-api.md)]
 
-::: zone-end
-
 ## Next steps
 
 * [Learn about entity components](concepts/entity-components.md)

articles/ai-services/language-service/conversational-language-understanding/quickstart.md

@@ -6,7 +6,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 04/29/2025
+ms.date: 05/23/2025
 ms.author: jboback
 ms.custom: language-service-ner
 ---

articles/ai-services/language-service/named-entity-recognition/concepts/named-entity-categories.md

@@ -6,7 +6,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 04/29/2025
+ms.date: 05/23/2025
 ms.author: jboback
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python

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

@@ -847,15 +847,6 @@ groups:
     title: Language Studio
   - id: rest-api
     title: REST API
-- id: usage-custom-language-features-foundry
-# Owner jboback
-  title: Usage methods
-  prompt: Choose your preferred usage method
-  pivots:
-  - id: azure-ai-foundry
-    title: Azure AI Foundry
-  - id: rest-api
-    title: REST API
 - id: programming-languages-text-analytics
 # Owner jboback
   title: Programming languages