@@ -153,6 +153,43 @@ CORS should now be configured to use the storage account from Document Intellige
 > [!NOTE]
 > By default, the Studio will use documents that are located at the root of your container. However, you can use data organized in folders by specifying the folder path in the Custom form project creation steps. *See* [**Organize your data in subfolders**](../how-to-guides/build-a-custom-model.md?view=doc-intel-2.1.0&preserve-view=true#organize-your-data-in-subfolders-optional)
 
+## Use Document Intelligence Studio features
+
+### Auto label documents with prebuilt models or one of your own models
+
+* In custom extraction model labeling page, you can now auto label your documents using one of Document Intelligent Service prebuilt models or your trained models.
+
+    :::image type="content" source="../media/studio/auto-label.gif" alt-text="Animated screenshot showing auto labeling in Studio.":::
+
+* For some documents, duplicate labels after running autolabel are possible. Make sure to modify the labels so that there are no duplicate labels in the labeling page afterwards.
+
+    :::image type="content" source="../media/studio/duplicate-labels.png" alt-text="Screenshot showing duplicate label warning after auto labeling.":::
+
+### Auto label tables
+
+* In custom extraction model labeling page, you can now auto label the tables in the document without having to label the tables manually.
+
+    :::image type="content" source="../media/studio/auto-table-label.gif" alt-text="Animated screenshot showing auto table labeling in Studio.":::
+
+### Add test files directly to your training dataset
+
+* Once you train a custom extraction model, make use of the test page to improve your model quality by uploading test documents to training dataset if needed.
+
+* If a low confidence score is returned for some labels, make sure to correctly label your content. If not, add them to the training dataset and relabel to improve the model quality.
+
+    :::image type="content" source="../media/studio/add-from-test.gif" alt-text="Animated screenshot showing how to add test files to training dataset.":::
+
+### Make use of the document list options and filters in custom projects
+
+* Use the custom extraction model labeling page to navigate through your training documents with ease by making use of the search, filter, and sort by feature.
+
+* Utilize the grid view to preview documents or use the list view to scroll through the documents more easily.
+
+    :::image type="content" source="../media/studio/document-options.png" alt-text="Screenshot of document list view options and filters.":::
+
+### Project sharing
+
+Share custom extraction projects with ease. For more information, see [Project sharing with custom models](../how-to-guides/project-share-custom-models.md).
 
 ## Next steps
 

@@ -1,12 +1,12 @@
 ---
 title: Studio experience for Document Intelligence
 titleSuffix: Azure AI services
-description: Learn how to set up and use either Document Intelligence Studio or AI Studio to test features of Azure AI Document Intelligence on the web.
+description: Learn how to set up and use either Document Intelligence Studio or AI Studio to test features of Azure AI Document Intelligence.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.topic: overview
-ms.date: 08/21/2024
+ms.topic: how-to
+ms.date: 10/29/2024
 ms.author: lajanuar
 monikerRange: '>=doc-intel-3.0.0'
 ---
@@ -206,7 +206,7 @@ Share custom extraction projects with ease. For more information, see [Project s
 
 Document Intelligence is part of the Azure AI services offerings in the Azure AI Studio. Each of the Azure AI services helps developers and organizations rapidly create intelligent, cutting-edge, market-ready, and responsible applications with out-of-the-box and prebuilt and customizable APIs and models.
 
-Learn how to [connect your AI services hub](../../ai-studio/ai-services/connect-ai-services.md) with AI services and get started using Document Intelligence.
+Learn how to [connect your AI services hub](../../ai-studio/ai-services/how-to/connect-ai-services.md) with AI services and get started using Document Intelligence.
 
 ## Next steps
 

@@ -9,7 +9,7 @@ ms.custom:
   - ignite-2023
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 12/19/2023
+ms.date: 11/04/2024
 ms.author: jboback
 ---
 
@@ -24,6 +24,7 @@ Language service provides each container with a common configuration framework,
 * Summarization
 * Named Entity Recognition (NER)
 * Personally Identifiable (PII) detection
+* Conversational Language Understanding (CLU)
 
 ## Configuration settings
 
@@ -49,7 +50,7 @@ The `Billing` setting specifies the endpoint URI of the _Language_ resource on A
 |Yes| `Billing` | String | Billing endpoint URI. |
 
 
-## Eula setting
+## EULA setting
 
 [!INCLUDE [Container shared configuration eula settings](../../includes/cognitive-services-containers-configuration-shared-settings-eula.md)]
 
@@ -71,11 +72,11 @@ Use bind mounts to read and write data to and from the container. You can specif
 
 The Language service containers don't use input or output mounts to store training or service data. 
 
-The exact syntax of the host mount location varies depending on the host operating system. Additionally, the host computer's mount location may not be accessible due to a conflict between permissions used by the docker service account and the host mount location permissions. 
+The exact syntax of the host mount location varies depending on the host operating system. The host computer's mount location may not be accessible due to a conflict between the docker service account permissions and the host mount location permissions. 
 
 |Optional| Name | Data type | Description |
 |-------|------|-----------|-------------|
-|Not allowed| `Input` | String | Language service containers do not use this.|
+|Not allowed| `Input` | String | Language service containers don't use this.|
 |Optional| `Output` | String | The target of the output mount. The default value is `/output`. This is the location of the logs. This includes container logs. <br><br>Example:<br>`--mount type=bind,src=c:\output,target=/output`|
 
 ## Next steps

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 12/19/2023
+ms.date: 11/04/2024
 ms.author: jboback
 ms.custom: language-service-clu
 ---
@@ -22,7 +22,7 @@ ms.custom: language-service-clu
 > * [Custom named entity recognition (NER)](../../custom-named-entity-recognition/overview.md)
 > * [Orchestration workflow](../../orchestration-workflow/overview.md)
 
-Custom language service features enable you to deploy your project to more than one region. This capability makes it much easier to access your project globally while you manage only one instance of your project in one place.
+Custom language service features enable you to deploy your project to more than one region. This capability makes it much easier to access your project globally while you manage only one instance of your project in one place. As of November 2024, custom language service features also enable you to deploy your project to multiple resources within a single region via the API, so that you can use your custom model wherever you need.
 
 Before you deploy a project, you can assign *deployment resources* in other regions. Each deployment resource is a different Language resource from the one that you use to author your project. You deploy to those resources and then target your prediction requests to that resource in their respective regions and your queries are served directly from that region.
 
@@ -54,13 +54,6 @@ You can only swap deployments that are available in the exact same regions. Othe
 
 If you remove an assigned resource from your project, all of the project deployments to that resource are deleted.
 
-> [!NOTE]
-> Orchestration workflow only:
->
-> You *can't* assign deployment resources to orchestration workflow projects with custom question answering or LUIS connections. Subsequently, you can't add custom question answering or LUIS connections to projects that have assigned resources.
->
-> For multiregion deployment to work as expected, the connected CLU projects *must also be deployed* to the same regional resources to which you deployed the orchestration workflow project. Otherwise, the orchestration workflow project attempts to route a request to a deployment in its region that doesn't exist.
-
 Some regions are only available for deployment and not for authoring projects.
 
 ## Related content

@@ -0,0 +1,147 @@
+---
+title: Use conversational language understanding (CLU) Docker containers on-premises
+titleSuffix: Azure AI services
+description: Use Docker containers for the conversational language understanding (CLU) API to determine the language of written text, on-premises.
+#services: cognitive-services
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.custom:
+ms.topic: how-to
+ms.date: 10/07/2024
+ms.author: jboback
+keywords: on-premises, Docker, container
+---
+
+# Install and run Conversational Language Understanding (CLU) containers
+
+> [!NOTE]
+> The data limits in a single synchronous API call for the CLU container are 5120 characters per document and up to 10 documents per call.
+
+Containers enable you to host the CLU API on your own infrastructure. If you have security or data governance requirements that can't be fulfilled by calling CLU remotely, then containers might be a good option.
+
+If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/cognitive-services/) before you begin.
+
+## Prerequisites
+
+You must meet the following prerequisites before using CLU containers. 
+
+* If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/cognitive-services/).
+* [Docker](https://docs.docker.com/) installed on a host computer. Docker must be configured to allow the containers to connect with and send billing data to Azure. 
+    * On Windows, Docker must also be configured to support Linux containers.
+    * You should have a basic understanding of [Docker concepts](https://docs.docker.com/get-started/overview/). 
+* A <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">Language resource </a>
+
+[!INCLUDE [Gathering required parameters](../../../containers/includes/container-gathering-required-parameters.md)]
+
+## Host computer requirements and recommendations
+
+[!INCLUDE [Host Computer requirements](../../../includes/cognitive-services-containers-host-computer.md)]
+
+The following table describes the minimum and recommended specifications for the available container. Each CPU core must be at least 2.6 gigahertz (GHz) or faster.
+
+It is recommended to have a CPU with AVX-512 instruction set, for the best experience (performance and accuracy).
+
+|                     | Minimum host specs     | Recommended host specs |
+|---------------------|------------------------|------------------------|
+| **CLU**   | 1 core, 2GB memory     | 4 cores, 8GB memory    |
+
+CPU core and memory correspond to the `--cpus` and `--memory` settings, which are used as part of the `docker run` command.
+
+## Get the container image with `docker pull`
+
+The CLU container image can be found on the `mcr.microsoft.com` container registry syndicate. It resides within the `azure-cognitive-services/textanalytics/` repository and is named `clu`. The fully qualified container image name is, `mcr.microsoft.com/azure-cognitive-services/textanalytics/clu`
+
+ To use the latest version of the container, you can use the `latest` tag, which is for English. You can also find a full list of containers for supported languages using the [tags on the MCR](https://mcr.microsoft.com/product/azure-cognitive-services/textanalytics/clu/tags).
+
+The latest CLU container is available in several languages. To download the container for the English container, use the command below. 
+
+```
+docker pull mcr.microsoft.com/azure-cognitive-services/textanalytics/clu:latest
+```
+
+[!INCLUDE [Tip for using docker list](../../../includes/cognitive-services-containers-docker-list-tip.md)]
+
+## Run the container with `docker run`
+
+Once the container is on the host computer, use the [docker run](https://docs.docker.com/engine/reference/commandline/run/) command to run the containers. The container will continue to run until you stop it. Replace the placeholders below with your own values:
+
+
+> [!IMPORTANT]
+> * The docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this based on your host operating system's requirements. 
+> * The `Eula`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start.  For more information, see [Billing](#billing).
+
+To run the CLU container, execute the following `docker run` command. Replace the placeholders below with your own values:
+
+| Placeholder | Value | Format or example |
+|-------------|-------|---|
+| **{API_KEY}** | The key for your Language resource. You can find it on your resource's **Key and endpoint** page, on the Azure portal. |`xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx`|
+| **{ENDPOINT_URI}** | The endpoint for accessing the API. You can find it on your resource's **Key and endpoint** page, on the Azure portal. | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
+| **{IMAGE_TAG}** | The image tag representing the language of the container you want to run. Make sure this matches the `docker pull` command you used. | `latest` |
+
+```bash
+docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \
+mcr.microsoft.com/azure-cognitive-services/textanalytics/clu:{IMAGE_TAG} \
+Eula=accept \
+Billing={ENDPOINT_URI} \
+ApiKey={API_KEY}
+```
+
+This command:
+
+* Runs a *CLU* container from the container image
+* Allocates one CPU core and 8 gigabytes (GB) of memory
+* Exposes TCP port 5000 and allocates a pseudo-TTY for the container
+* Automatically removes the container after it exits. The container image is still available on the host computer.
+
+[!INCLUDE [Running multiple containers on the same host](../../../includes/cognitive-services-containers-run-multiple-same-host.md)]
+
+## Query the container's prediction endpoint
+
+The container provides REST-based query prediction endpoint APIs.
+
+Use the host, `http://localhost:5000`, for container APIs.
+
+<!-- ## Validate container is running -->
+
+[!INCLUDE [Container's API documentation](../../../includes/cognitive-services-containers-api-documentation.md)]
+
+For information on how to call CLU see [our guide](call-api.md).
+
+## Run the container disconnected from the internet
+
+[!INCLUDE [configure-disconnected-container](../../../containers/includes/configure-disconnected-container.md)]
+
+## Stop the container
+
+[!INCLUDE [How to stop the container](../../../includes/cognitive-services-containers-stop.md)]
+
+## Troubleshooting
+
+If you run the container with an output [mount](../../concepts/configure-containers.md#mount-settings) and logging enabled, the container generates log files that are helpful to troubleshoot issues that happen while starting or running the container.
+
+[!INCLUDE [Azure AI services FAQ note](../../../containers/includes/cognitive-services-faq-note.md)]
+
+## Billing
+
+The CLU containers send billing information to Azure, using a _Language_ resource on your Azure account.
+
+[!INCLUDE [Container's Billing Settings](../../../includes/cognitive-services-containers-how-to-billing-info.md)]
+
+For more information about these options, see [Configure containers](../../concepts/configure-containers.md).
+
+## Summary
+
+In this article, you learned concepts and workflow for downloading, installing, and running CLU containers. In summary:
+
+* CLU provides Linux containers for Docker
+* Container images are downloaded from the Microsoft Container Registry (MCR).
+* Container images run in Docker.
+* You must specify billing information when instantiating a container.
+
+> [!IMPORTANT]
+> Azure AI containers are not licensed to run without being connected to Azure for metering. Customers need to enable the containers to communicate billing information with the metering service at all times. Azure AI containers do not send customer data (e.g. text that is being analyzed) to Microsoft.
+
+## Next steps
+
+* See [Configure containers](../../concepts/configure-containers.md) for configuration settings.

@@ -11,4 +11,4 @@
 ---
 
 > [!TIP]
-> You can use [**Language Studio**](../language-studio.md) to try Language service features without needing to write code. 
+> You can use [**AI Studio**](../../../ai-studio/what-is-ai-studio.md) to try summarization without needing to write code. 

@@ -35,11 +35,16 @@ If you have content expressed in a less frequently used language, you can try La
 | Basque              | `eu`          | `Latn`                |
 | Belarusian          | `be`          | `Cyrl`                |
 | Bengali             | `bn`          | `Beng`, `Latn`        |
+| Bhojpuri            | `bho`         | `Deva`                |
+| Bodo                | `brx`         | `Deva`                |
 | Bosnian             | `bs`          | `Latn`                |
 | Bulgarian           | `bg`          | `Cyrl`                |
 | Burmese             | `my`          | `Mymr`                |
 | Catalan             | `ca`          | `Latn`                |
 | Central Khmer       | `km`          | `Khmr`                |
+| Checheni            | `ce`          | `Cyrl`                |
+| Chhattisgarhi       | `hne`         | `Deva`                |
+| Chinese Literal     | `lzh`         | `Hani`                |
 | Chinese Simplified  | `zh_chs`      | `Hans`                |
 | Chinese Traditional | `zh_cht`      | `Hant`                |
 | Chuvash             | `cv`          | `Cyrl`                |
@@ -49,6 +54,7 @@ If you have content expressed in a less frequently used language, you can try La
 | Danish              | `da`          | `Latn`                |
 | Dari                | `prs`         | `Arab`                |
 | Divehi              | `dv`          | `Thaa`                |
+| Dogri               | `dgo`         | `Deva`                |
 | Dutch               | `nl`          | `Latn`                |
 | English             | `en`          | `Latn`                |
 | Esperanto           | `eo`          | `Latn`                |
@@ -72,29 +78,36 @@ If you have content expressed in a less frequently used language, you can try La
 | Igbo                | `ig`          | `Latn`                |
 | Indonesian          | `id`          | `Latn`                |
 | Inuktitut           | `iu`          | `Cans`, `Latn`        |
+| Inuinnaqtun         | `ikt`         | `Latn`                |
 | Irish               | `ga`          | `Latn`                |
 | Italian             | `it`          | `Latn`                |
 | Japanese            | `ja`          | `Jpan`                |
 | Javanese            | `jv`          | `Latn`                |
 | Kannada             | `kn`          | `Knda`, `Latn`        |
+| Kashmiri            | `ks`          | `Arab`, `Deva`, `Shrd`|
 | Kazakh              | `kk`          | `Cyrl`                |
 | Kinyarwanda         | `rw`          | `Latn`                |
 | Kirghiz             | `ky`          | `Cyrl`                |
+| Konkani             | `gom`         | `Deva`                |
 | Korean              | `ko`          | `Hang`                |
 | Kurdish             | `ku`          | `Arab`                |
+| Kurdish (Northern)  | `kmr`         | `Latn`                |
 | Lao                 | `lo`          | `Laoo`                |
 | Latin               | `la`          | `Latn`                |
 | Latvian             | `lv`          | `Latn`                |
 | Lithuanian          | `lt`          | `Latn`                |
+| Lower Siberian      | `dsb`         | `Latn`                |
 | Luxembourgish       | `lb`          | `Latn`                |
 | Macedonian          | `mk`          | `Cyrl`                |
+| Maithili            | `mai`         | `Deva`                |
 | Malagasy            | `mg`          | `Latn`                |
 | Malay               | `ms`          | `Latn`                |
 | Malayalam           | `ml`          | `Mlym`, `Latn`        |
 | Maltese             | `mt`          | `Latn`                |
 | Maori               | `mi`          | `Latn`                |
 | Marathi             | `mr`          | `Deva`, `Latn`        |
-| Mongolian           | `mn`          | `Cyrl`                |
+| Meitei              | `mni`         | `Mtei`                |
+| Mongolian           | `mn`          | `Cyrl`, `Mong`        |
 | Nepali              | `ne`          | `Deva`                |
 | Norwegian           | `no`          | `Latn`                |
 | Norwegian Nynorsk   | `nn`          | `Latn`                |
@@ -108,6 +121,8 @@ If you have content expressed in a less frequently used language, you can try La
 | Romanian            | `ro`          | `Latn`                |
 | Russian             | `ru`          | `Cyrl`                |
 | Samoan              | `sm`          | `Latn`                |
+| Sanscrit            | `sa`          | `Deva`                |
+| Santali             | `sat`         | `Olck`                |
 | Serbian             | `sr`          | `Latn`, `Cyrl`        |
 | Shona               | `sn`          | `Latn`                |
 | Sindhi              | `sd`          | `Arab`                |

@@ -1,28 +1,28 @@
 ---
-title: Preview API overview
+title: Version-based API mapping
 titleSuffix: Azure AI services
-description: Learn about the NER preview API.
+description: Learn about the differences between NER API versions.
 #services: cognitive-services
 author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 12/19/2023
+ms.date: 11/04/2024
 ms.author: jboback
 ms.custom: language-service-ner
 ---
 
 # Preview API changes
 
-Use this article to get an overview of the new API changes starting from `2023-04-15-preview` version. This API change mainly introduces two new concepts (`entity types` and `entity tags`) replacing the `category` and `subcategory` fields in the current Generally Available API. A detailed overview of each API parameter and the supported API versions it corresponds to can be found on the [Skill Parameters][../how-to/skill-parameters.md] page
+Use this article to get an overview of the new API changes starting from version `2024-11-01`. This API change mainly introduces two new concepts (`entity types` and `entity tags`) replacing the `category` and `subcategory` fields in the current Generally Available API. A detailed overview of each API parameter and the supported API versions it corresponds to can be found on the [Skill Parameters][../how-to/skill-parameters.md] page
 
 ## Entity types
 Entity types represent the lowest (or finest) granularity at which the entity has been detected and can be considered to be the base class that has been detected.
 
 ## Entity tags
 Entity tags are used to further identify an entity where a detected entity is tagged by the entity type and additional tags to differentiate the identified entity. The entity tags list could be considered to include categories, subcategories, sub-subcategories, and so on.
 
-## Changes from generally available API to preview API
+## Changes from versions `2022-05-01` and `2023-04-01` to version `2024-11-01` API
 The changes introduce better flexibility for the named entity recognition service, including:
 
 Updates to the structure of input formats:
@@ -34,11 +34,11 @@ Updates to the handling of output formats:
 
 * More granular entity recognition outputs through introducing the tags list where an entity could be tagged by more than one entity tag.
 * Overlapping entities where entities could be recognized as more than one entity type and if so, this entity would be returned twice. If an entity was recognized to belong to two entity tags under the same entity type, both entity tags are returned in the tags list.
-* Filtering entities using entity tags, you can learn more about this by navigating to [this article](../how-to-call.md#select-which-entities-to-be-returned-preview-api-only).
+* Filtering entities using entity tags, you can learn more about this by navigating to [this article](../how-to-call.md#select-which-entities-to-be-returned).
 * Metadata Objects which contain additional information about the entity but currently only act as a wrapper for the existing entity resolution feature. You can learn more about this new feature [here](entity-metadata.md).
 
-## Generally available to preview API entity mappings
-You can see a comparison between the structure of the entity categories/types in the [Supported Named Entity Recognition (NER) entity categories and entity types article](./named-entity-categories.md). Below is a table describing the mappings between the results you would expect to see from the Generally Available API and the Preview API.
+## Versions `2022-05-01` and `2023-04-01` to current version API entity mappings
+You can see a comparison between the structure of the entity categories/types in the [Supported Named Entity Recognition (NER) entity categories and entity types article](./named-entity-categories.md). Below is a table describing the mappings between the results you would expect to see from versions `2022-05-01` and `2023-04-01` and the current version API.
 
 | Type           | Tags                                   |
 |----------------|----------------------------------------|

@@ -1,7 +1,7 @@
 ---
 title: How to perform Named Entity Recognition (NER)
 titleSuffix: Azure AI services
-description: This article will show you how to extract named entities from text.
+description: This article shows you how to extract named entities from text.
 #services: cognitive-services
 author: jboback
 manager: nitinme
@@ -37,16 +37,16 @@ The API attempts to detect the [defined entity categories](concepts/named-entity
 
 ## Getting NER results
 
-When you get results from NER, you can stream the results to an application or save the output to a file on the local system. The API response will include [recognized entities](concepts/named-entity-categories.md), including their categories and subcategories, and confidence scores. 
+When you get results from NER, you can stream the results to an application or save the output to a file on the local system. The API response includes [recognized entities](concepts/named-entity-categories.md), including their categories and subcategories, and confidence scores. 
 
-## Select which entities to be returned (Preview API only)
+## Select which entities to be returned
 
-Starting with **API version 2023-04-15-preview**, the API attempts to detect the [defined entity types and tags](concepts/named-entity-categories.md) for a given document language. The entity types and tags replace the categories and subcategories structure the older models use to define entities for more flexibility. You can also specify which entities are detected and returned, use the optional `includeList` and `excludeList` parameters with the appropriate entity types. The following example would detect only `Location`. You can specify one or more [entity types](concepts/named-entity-categories.md) to be returned. Given the types and tags hierarchy introduced for this version, you have the flexibility to filter on different granularity levels as so:
+The API attempts to detect the [defined entity types and tags](concepts/named-entity-categories.md) for a given document language. The entity types and tags replace the categories and subcategories structure the older models use to define entities for more flexibility. You can also specify which entities are detected and returned, use the optional `includeList` and `excludeList` parameters with the appropriate entity types. The following example would detect only `Location`. You can specify one or more [entity types](concepts/named-entity-categories.md) to be returned. Given the types and tags hierarchy introduced for this version, you have the flexibility to filter on different granularity levels as so:
 
 **Input:**
 
 > [!NOTE]
-> In this example, it returns only the **Location** entity type.
+> In this example, it returns only the **"Location"** entity type.
 
 ```bash
 {
@@ -108,6 +108,80 @@ This method returns all `Location` entities only falling under the `GPE` tag and
 
 Using these parameters we can successfully filter on only `Location` entity types, since the `GPE` entity tag included in the `includeList` parameter, falls under the `Location` type. We then filter on only Geopolitical entities and exclude any entities tagged with `Continent` or `CountryRegion` tags.
 
+## Additional output attributes
+
+In order to provide users with more insight into an entity's types and provide increased usability, NER supports these attributes in the output:
+
+|Name of the attribute|Type        |Definition                               |
+|---------------------|------------|-----------------------------------------|
+|`type`               |String      |The most specific type of detected entity.<br><br>For example, “Seattle” is a `City`, a `GPE` (Geo Political Entity) and a `Location`. The most granular classification for “Seattle” is that it is a `City`. The type would be `City` for the text “Seattle".|
+|`tags`               |List (tags) |A list of tag objects which expresses the affinity of the detected entity to a hierarchy or any other grouping.<br><br>A tag contains two fields:<br>1. `name`: A unique name for the tag.<br>2. `confidenceScore`: The associated confidence score for a tag ranging from 0 to 1.<br><br>This unique tagName is be used to filter in the `inclusionList` and `exclusionList` parameters.
+|`metadata`           |Object      |Metadata is an object containing more data about the entity type detected. It changes based on the field `metadataKind`.
+
+## Sample output
+
+This sample output includes an example of the additional output attributes.
+
+```bash
+{ 
+    "kind": "EntityRecognitionResults", 
+    "results": { 
+        "documents": [ 
+            { 
+                "id": "1", 
+                "entities": [ 
+                    { 
+                        "text": "Microsoft", 
+                        "category": "Organization", 
+                        "type": "Organization", 
+                        "offset": 0, 
+                        "length": 9, 
+                        "confidenceScore": 0.97, 
+                        "tags": [ 
+                            { 
+                                "name": "Organization", 
+                                "confidenceScore": 0.97 
+                            } 
+                        ] 
+                    }, 
+                    { 
+                        "text": "One", 
+                        "category": "Quantity", 
+                        "type": "Number", 
+                        "subcategory": "Number", 
+                        "offset": 21, 
+                        "length": 3, 
+                        "confidenceScore": 0.9, 
+                        "tags": [ 
+                            { 
+                                "name": "Number", 
+                                "confidenceScore": 0.8 
+                            }, 
+                            { 
+                                "name": "Quantity", 
+                                "confidenceScore": 0.8 
+                            }, 
+                            { 
+                                "name": "Numeric", 
+                                "confidenceScore": 0.8 
+                            } 
+                        ], 
+                        "metadata": { 
+                            "metadataKind": "NumberMetadata", 
+                            "numberKind": "Integer", 
+                            "value": 1.0 
+                        } 
+                    } 
+                ], 
+                "warnings": [] 
+            } 
+        ], 
+        "errors": [], 
+        "modelVersion": "2023-09-01" 
+    } 
+} 
+```
+
 ## Specify the NER model
 
 By default, this feature uses the latest available AI model on your text. You can also configure your API requests to use a specific [model version](../concepts/model-lifecycle.md).

@@ -8,43 +8,65 @@ manager: nitinme
 ms.service: azure-ai-language
 ms.custom:
 ms.topic: how-to
-ms.date: 03/21/2024
+ms.date: 11/04/2024
 ms.author: jboback
 ---
 
 # Learn about named entity recognition skill parameters
 
-Use this article to get an overview of the different API parameters used to adjust the input to a NER API call.
+Use this article to get an overview of the different API parameters used to adjust the input to a Named Entity Recognition (NER) API call.
 
 ## InclusionList parameter
 
-The “inclusionList” parameter allows for you to specify which of the NER entity tags, listed here [link to Preview API table], you would like included in the entity list output in your inference JSON listing out all words and categorizations recognized by the NER service. By default, all recognized entities will be listed.
+The `inclusionList` parameter allows for you to specify which of the NER entity tags, you would like included in the entity list output in your inference JSON listing out all words and categorizations recognized by the NER service. By default, all recognized entities are listed.
 
 ## ExclusionList parameter
 
-The “exclusionList” parameter allows for you to specify which of the NER entity tags, listed here [link to Preview API table], you would like excluded in the entity list output in your inference JSON listing out all words and categorizations recognized by the NER service. By default, all recognized entities will be listed.
-
-<!--
-## Example
-
-To do: work with Bidisha & Mikael to update with a good example
--->
+The `exclusionList` parameter allows for you to specify which of the NER entity tags, you would like excluded in the entity list output in your inference JSON listing out all words and categorizations recognized by the NER service. By default, all recognized entities are listed.
 
 ## overlapPolicy parameter
 
-The “overlapPolicy” parameter allows for you to specify how you like the NER service to respond to recognized words/phrases that fall into more than one category. 
+The `overlapPolicy` parameter allows for you to specify how you like the NER service to respond to recognized words/phrases that fall into more than one category. 
 
-By default, the overlapPolicy parameter will be set to “matchLongest”. This option will categorize the extracted word/phrase under the entity category that can encompass the longest span of the extracted word/phrase (longest defined by the most number of characters included).
+By default, the `overlapPolicy` parameter is set to `matchLongest`. This option categorizes the extracted word/phrase under the entity category that can encompass the longest span of the extracted word/phrase (longest defined by the most number of characters included).
 
-The alternative option for this parameter is “allowOverlap”, where all possible entity categories will be listed. 
+The alternative option for this parameter is `allowOverlap`, where all possible entity categories are listed. 
 Parameters by supported API version
 
-|Parameter                                                   |API versions which support            |
-|------------------------------------------------------------|--------------------------------------|
-|inclusionList                                               |2023-04-15-preview, 2023-11-15-preview|
-|exclusionList                                               |2023-04-15-preview, 2023-11-15-preview|
-|Overlap policy                                              |2023-04-15-preview, 2023-11-15-preview|
-|[Entity resolution](link to archived Entity Resolution page)|2022-10-01-preview                    |
+## inferenceOptions parameter
+
+Defines a selection of options available for adjusting the inference. Currently we have only one property called `excludeNormalizedValues` which excludes the detected entity values to be normalized and included in the metadata. The numeric and temporal entity types support value normalization. 
+
+## Sample
+
+This bit of sample code explains how to use skill parameters.
+
+```bash
+{ 
+    "analysisInput": { 
+        "documents": [ 
+            { 
+                "id": "1", 
+                "text": "My name is John Doe", 
+                "language": "en" 
+            } 
+        ] 
+    }, 
+    "kind": "EntityRecognition", 
+    "parameters": { 
+        "overlapPolicy": { 
+            "policyKind": "AllowOverlap" //AllowOverlap|MatchLongest(default) 
+        }, 
+        "inferenceOptions": { 
+            "excludeNormalizedValues": true //(Default: false) 
+        }, 
+        "inclusionList": [ 
+            "DateAndTime" // A list of entity tags to be used to allow into the response. 
+        ], 
+        "exclusionList": ["Date"] // A list of entity tags to be used to filter out from the response. 
+    } 
+} 
+```
 
 ## Next steps
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 06/20/2024
+ms.date: 11/19/2024
 ms.author: lajanuar
 ---
 
@@ -21,8 +21,6 @@ ms.author: lajanuar
 
 > [!IMPORTANT]
 >
-> * Native document support is a gated preview. To request access to the native document support feature, complete and submit the [**Apply for access to Language Service previews**](https://aka.ms/gating-native-document) form.
->
 > * Azure AI Language public preview releases provide early access to features that are in active development.
 > * Features, approaches, and processes may change, prior to General Availability (GA), based on user feedback.
 
@@ -59,7 +57,7 @@ A native document refers to the file format used to create the original document
 |Attribute|Input limit|
 |---|---|
 |**Total number of documents per request** |**≤ 20**|
-|**Total content size per request**| **≤ 1 MB**|
+|**Total content size per request**| **≤ 10 MB**|
 
 ## Include native documents with an HTTP request
 
@@ -169,32 +167,39 @@ For this quickstart, you need a **source document** uploaded to your **source co
   ***Request sample***
 
 ```json
-{
-    "displayName": "Extracting Location & US Region",
-    "analysisInput": {
-        "documents": [
-            {
-                "language": "en-US",
-                "id": "Output-excel-file",
-                "source": {
-                    "location": "{your-source-blob-with-SAS-URL}"
-                },
-                "target": {
-                    "location": "{your-target-container-with-SAS-URL}"
-                }
+{ 
+    "displayName": "Document PII Redaction example", 
+    "analysisInput": { 
+        "documents": [ 
+            { 
+                "language": "en-US", 
+                "id": "Output-1", 
+                "source": { 
+                    "location": "{your-source-blob-with-SAS-URL}" 
+                }, 
+                "target": { 
+                    "location": "{your-target-container-with-SAS-URL}" 
+                } 
             } 
-        ]
-    },
-    "tasks": [
-        {
-            "kind": "PiiEntityRecognition",
-            "parameters":{
-                "excludePiiCategories" : ["PersonType", "Category2", "Category3"],
-                "redactionPolicy": "UseRedactionCharacterWithRefId" 
-            }
-        }
-    ]
-}
+        ] 
+    }, 
+    "tasks": [ 
+        { 
+            "kind": "PiiEntityRecognition", 
+            "taskName": "Redact PII Task 1", 
+            "parameters": { 
+                "redactionPolicy": { 
+                    "policyKind": "entityMask"  // Optional. Defines redactionPolicy; changes behavior based on value. Options: noMask, characterMask (default), and entityMask. 
+                }, 
+                "piiCategories": [ 
+                    "Person", 
+                    "Organization" 
+                ], 
+                "excludeExtractionData": false  // Default is false. If true, only the redacted document is stored, without extracted entities data. 
+            } 
+        } 
+    ] 
+} 
 ```
 
 * The source `location` value is the SAS URL for the **source document (blob)**, not the source container SAS URL.
@@ -206,7 +211,7 @@ For this quickstart, you need a **source document** uploaded to your **source co
 1. Here's the preliminary structure of the POST request:
 
    ```bash
-      POST {your-language-endpoint}/language/analyze-documents/jobs?api-version=2023-11-15-preview
+      POST {your-language-endpoint}/language/analyze-documents/jobs?api-version=2024-11-15-preview
    ```
 
 1. Before you run the **POST** request, replace `{your-language-resource-endpoint}` and `{your-key}` with the values from your Azure portal Language service instance.
@@ -217,21 +222,21 @@ For this quickstart, you need a **source document** uploaded to your **source co
     ***PowerShell***
 
     ```powershell
-       cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2023-11-15-preview" -i -X POST --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@pii-detection.json"
+       cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2024-11-15-preview" -i -X POST --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@pii-detection.json"
     ```
 
     ***command prompt / terminal***
 
      ```bash
-        curl -v -X POST "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2023-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@pii-detection.json"
+        curl -v -X POST "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2024-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@pii-detection.json"
      ```
 
 1. Here's a sample response:
 
    ```http
    HTTP/1.1 202 Accepted
    Content-Length: 0
-   operation-location: https://{your-language-resource-endpoint}/language/analyze-documents/jobs/f1cc29ff-9738-42ea-afa5-98d2d3cabf94?api-version=2023-11-15-preview
+   operation-location: https://{your-language-resource-endpoint}/language/analyze-documents/jobs/f1cc29ff-9738-42ea-afa5-98d2d3cabf94?api-version=2024-11-15-preview
    apim-request-id: e7d6fa0c-0efd-416a-8b1e-1cd9287f5f81
    x-ms-region: West US 2
    Date: Thu, 25 Jan 2024 15:12:32 GMT
@@ -250,7 +255,7 @@ You receive a 202 (Success) response that includes a read-only Operation-Locatio
 1. Here's the preliminary structure of the **GET** request:
 
    ```bash
-     GET {your-language-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview
+     GET {your-language-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2024-11-15-preview
    ```
 
 1. Before you run the command, make these changes:
@@ -262,11 +267,11 @@ You receive a 202 (Success) response that includes a read-only Operation-Locatio
 ### Get request
 
 ```powershell
-    cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview" -i -X GET --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
+    cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2024-11-15-preview" -i -X GET --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
 ```
 
 ```bash
-    curl -v -X GET "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
+    curl -v -X GET "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2024-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
 ```
 
 #### Examine the response
@@ -373,21 +378,21 @@ Before you run the **POST** request, replace `{your-language-resource-endpoint}`
   ***PowerShell***
 
   ```powershell
-   cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2023-11-15-preview" -i -X POST --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@document-summarization.json"
+   cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2024-11-15-preview" -i -X POST --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@document-summarization.json"
   ```
 
   ***command prompt / terminal***
 
   ```bash
-  curl -v -X POST "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2023-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@document-summarization.json"
+  curl -v -X POST "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2024-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@document-summarization.json"
   ```
 
 Here's a sample response:
 
    ```http
    HTTP/1.1 202 Accepted
    Content-Length: 0
-   operation-location: https://{your-language-resource-endpoint}/language/analyze-documents/jobs/f1cc29ff-9738-42ea-afa5-98d2d3cabf94?api-version=2023-11-15-preview
+   operation-location: https://{your-language-resource-endpoint}/language/analyze-documents/jobs/f1cc29ff-9738-42ea-afa5-98d2d3cabf94?api-version=2024-11-15-preview
    apim-request-id: e7d6fa0c-0efd-416a-8b1e-1cd9287f5f81
    x-ms-region: West US 2
    Date: Thu, 25 Jan 2024 15:12:32 GMT
@@ -405,8 +410,8 @@ You receive a 202 (Success) response that includes a read-only Operation-Locatio
 
 1. Here's the structure of the **GET** request:
 
-   ```http
-   GET {cognitive-service-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview
+   ```bash
+   GET {cognitive-service-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2024-11-15-preview
    ```
 
 1. Before you run the command, make these changes:
@@ -418,11 +423,11 @@ You receive a 202 (Success) response that includes a read-only Operation-Locatio
 ### Get request
 
 ```powershell
-    cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview" -i -X GET --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
+    cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2024-11-15-preview" -i -X GET --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
 ```
 
 ```bash
-    curl -v -X GET "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
+    curl -v -X GET "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2024-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"
 ```
 
 #### Examine the response

@@ -1,13 +1,13 @@
 ---
 title: How to detect Personally Identifiable Information (PII) in conversations.
 titleSuffix: Azure AI services
-description: This article will show you how to extract PII from chat and spoken transcripts and redact identifiable information.
+description: This article shows you how to extract PII from chat and spoken transcripts and redact identifiable information.
 #services: cognitive-services
 author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 12/19/2023
+ms.date: 11/04/2024
 ms.author: jboback
 ms.reviewer: bidishac
 ---
@@ -22,7 +22,7 @@ For transcripts, the API also enables redaction of audio segments, which contain
 
 ### Specify the PII detection model
 
-By default, this feature will use the latest available AI model on your input. You can also configure your API requests to use a specific [model version](../concepts/model-lifecycle.md).
+By default, this feature uses the latest available AI model on your input. You can also configure your API requests to use a specific [model version](../concepts/model-lifecycle.md).
 
 ### Language support
 
@@ -43,17 +43,17 @@ When using the async feature, the API results are available for 24 hours from th
 
 When you submit data to conversational PII, you can send one conversation (chat or spoken) per request.
 
-The API will attempt to detect all the [defined entity categories](concepts/conversations-entity-categories.md) for a given conversation input. If you want to specify which entities will be detected and returned, use the optional `piiCategories` parameter with the appropriate entity categories.
+The API attempts to detect all the [defined entity categories](concepts/conversations-entity-categories.md) for a given conversation input. If you want to specify which entities are detected and returned, use the optional `piiCategories` parameter with the appropriate entity categories.
 
-For spoken transcripts, the entities detected will be returned on the `redactionSource` parameter value provided. Currently, the supported values for `redactionSource` are `text`, `lexical`, `itn`, and `maskedItn` (which maps to Speech to text REST API's `display`\\`displayText`, `lexical`, `itn` and `maskedItn` format respectively). Additionally, for the spoken transcript input, this API will also provide audio timing information to empower audio redaction. For using the audioRedaction feature, use the optional `includeAudioRedaction` flag with `true` value. The audio redaction is performed based on the lexical input format.
+For spoken transcripts, the entities detected are returned on the `redactionSource` parameter value provided. Currently, the supported values for `redactionSource` are `text`, `lexical`, `itn`, and `maskedItn` (which maps to Speech to text REST API's `display`\\`displayText`, `lexical`, `itn` and `maskedItn` format respectively). Additionally, for the spoken transcript input, this API also provides audio timing information to empower audio redaction. For using the audioRedaction feature, use the optional `includeAudioRedaction` flag with `true` value. The audio redaction is performed based on the lexical input format.
 
 > [!NOTE]
 > Conversation PII now supports 40,000 characters as document size.
 
 
 ## Getting PII results
 
-When you get results from PII detection, you can stream the results to an application or save the output to a file on the local system. The API response will include [recognized entities](concepts/conversations-entity-categories.md), including their categories and subcategories, and confidence scores. The text string with the PII entities redacted will also be returned.
+When you get results from PII detection, you can stream the results to an application or save the output to a file on the local system. The API response includes [recognized entities](concepts/conversations-entity-categories.md), including their categories and subcategories, and confidence scores. The text string with the PII entities redacted is also returned.
 
 ## Examples
 
@@ -77,6 +77,77 @@ When you get results from PII detection, you can stream the results to an applic
     
 # [REST API](#tab/rest-api)
 
+## Redaction Policy (version 2024-11-15-preview only)
+
+In version 2024-11-15-preview, you're able to define the `redactionPolicy` parameter to reflect the redaction policy to be used when redacting the document in the response. The policy field supports 3 policy types:
+
+- `noMask` 
+- `characterMask` (default) 
+- `entityMask` 
+
+The `noMask` policy allows the user to return the response without the `redactedText` field. 
+
+The `characterMask` policy allows the `redactedText` to be masked with a character, preserving the length and offset of the original text. This is the existing behavior.
+
+There is also an optional field called `redactionCharacter` where you can input the character to be used in redaction if you're using the `characterMask` policy 
+
+The `entityMask` policy allows you to mask the detected PII entity text with the detected entity type
+
+Use the following example if you want to change the redaction policy.
+
+```bash
+curl -i -X POST https://your-language-endpoint-here/language/analyze-conversations/jobs?api-version=2024-05-01 \
+-H "Content-Type: application/json" \
+-H "Ocp-Apim-Subscription-Key: your-key-here" \
+-d \
+'
+{ 
+    "displayName": "Analyze conversations from xxx", 
+    "analysisInput": { 
+        "conversations": [ 
+            { 
+                "id": "23611680-c4eb-4705-adef-4aa1c17507b5", 
+                "language": "en", 
+                "modality": "text", 
+                "conversationItems": [ 
+                    { 
+                        "participantId": "agent_1", 
+                        "id": "1", 
+                        "text": "Good morning." 
+                    }, 
+                    { 
+                        "participantId": "agent_1", 
+                        "id": "2", 
+                        "text": "Can I have your name?" 
+                    }, 
+                    { 
+                        "participantId": "customer_1", 
+                        "id": "3", 
+                        "text": "Sure that is John Doe." 
+                    } 
+                ] 
+            } 
+        ] 
+    }, 
+    "tasks": [ 
+        { 
+            "taskName": "analyze 1", 
+            "kind": "ConversationalPIITask", 
+            "parameters": { 
+                "modelVersion": "2023-04-15-preview", 
+                “redactionCharacter” 
+                "redactionPolicy": { 
+                    "policyKind": "characterMask", 
+                    //characterMask|entityMask|noMask 
+                    "redactionCharacter": "*" 
+                } 
+            } 
+        } 
+    ] 
+} 
+`
+```
+
 ## Submit transcripts using speech to text
 
 Use the following example if you have conversations transcribed using the Speech service's [speech to text](../../Speech-Service/speech-to-text.md) feature:
@@ -262,7 +333,7 @@ curl -i -X POST https://your-language-endpoint-here/language/analyze-conversatio
 
 ## Get the result
 
-Get the `operation-location` from the response header. The value will look similar to the following URL:
+Get the `operation-location` from the response header. The value looks similar to the following URL:
 
 ```rest
 https://your-language-endpoint/language/analyze-conversations/jobs/12345678-1234-1234-1234-12345678

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 10/21/2024
+ms.date: 11/04/2024
 ms.author: jboback
 ms.custom: language-service-pii
 ---
@@ -26,11 +26,27 @@ The PII feature can evaluate unstructured text, extract and redact sensitive inf
 
 ### Specify the PII detection model
 
-By default, this feature will use the latest available AI model on your text. You can also configure your API requests to use a specific [model version](../concepts/model-lifecycle.md).
+By default, this feature uses the latest available AI model on your text. You can also configure your API requests to use a specific [model version](../concepts/model-lifecycle.md).
 
 ### Input languages
 
-When you submit documents to be processed, you can specify which of [the supported languages](language-support.md) they're written in. if you don't specify a language, extraction will default to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md). 
+When you submit documents to be processed, you can specify which of [the supported languages](language-support.md) they're written in. if you don't specify a language, extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md). 
+
+### Redaction Policy (version 2024-11-5-preview only)
+
+in version 2024-11-5-preview, you're able to define the `redactionPolicy` parameter to reflect the redaction policy to be used when redacting the document in the response. The policy field supports 3 policy types:
+
+- `DoNotRedact` 
+- `MaskWithCharacter` (default) 
+- `MaskWithEntityType` 
+
+The `DoNotRedact` policy allows the user to return the response without the `redactedText` field. 
+
+The `MaskWithRedactionCharacter` policy allows the `redactedText` to be masked with a character, preserving the length and offset of the original text. This is the existing behavior.
+
+There is also an optional field called `redactionCharacter` where you can input the character to be used in redaction if you're using the `MaskWithCharacter` policy 
+
+The `MaskWithEntityType` policy allows you to mask the detected PII entity text with the detected entity type. 
 
 ## Submitting data
 
@@ -40,15 +56,15 @@ Analysis is performed upon receipt of the request. Using the PII detection featu
 
 ## Select which entities to be returned
 
-The API will attempt to detect the [defined entity categories](concepts/entity-categories.md) for a given document language. If you want to specify which entities will be detected and returned, use the optional `piiCategories` parameter with the appropriate entity categories. This parameter can also let you detect entities that aren't enabled by default for your document language. The following example would detect only `Person`. You can specify one or more [entity types](concepts/entity-categories.md) to be returned.
+The API attempts to detect the [defined entity categories](concepts/entity-categories.md) for a given document language. If you want to specify which entities are detected and returned, use the optional `piiCategories` parameter with the appropriate entity categories. This parameter can also let you detect entities that aren't enabled by default for your document language. The following example would detect only `Person`. You can specify one or more [entity types](concepts/entity-categories.md) to be returned.
 
 > [!TIP]
-> If you don't include `default` when specifying entity categories, The API will only return the entity categories you specify.
+> If you don't include `default` when specifying entity categories, The API only returns the entity categories you specify.
 
 **Input:**
 
 > [!NOTE]
-> In this example, it will return only **person** entity type:
+> In this example, it returns only the **person** entity type:
 
 `https://<your-language-resource-endpoint>/language/:analyze-text?api-version=2022-05-01`
 
@@ -73,7 +89,13 @@ The API will attempt to detect the [defined entity categories](concepts/entity-c
                 "text": "We went to Contoso foodplace located at downtown Seattle last week for a dinner party, and we adore the spot! They provide marvelous food and they have a great menu. The chief cook happens to be the owner (I think his name is John Doe) and he is super nice, coming out of the kitchen and greeted us all. We enjoyed very much dining in the place! The pasta I ordered was tender and juicy, and the place was impeccably clean. You can even pre-order from their online menu at www.contosofoodplace.com, call 112-555-0176 or send email to order@contosofoodplace.com! The only complaint I have is the food didn't come fast enough. Overall I highly recommend it!"
             }
         ]
-    }
+    },
+    "kind": "PiiEntityRecognition", 
+    "parameters": { 
+        "redactionPolicy": { 
+            "policyKind": "MaskWithCharacter"  
+             //MaskWithCharacter|MaskWithEntityType|DoNotRedact 
+            "redactionCharacter": "*"  
 }
 
 ```
@@ -109,7 +131,7 @@ The API will attempt to detect the [defined entity categories](concepts/entity-c
 
 ## Getting PII results
 
-When you get results from PII detection, you can stream the results to an application or save the output to a file on the local system. The API response will include [recognized entities](concepts/entity-categories.md), including their categories and subcategories, and confidence scores. The text string with the PII entities redacted will also be returned.
+When you get results from PII detection, you can stream the results to an application or save the output to a file on the local system. The API response includes [recognized entities](concepts/entity-categories.md), including their categories and subcategories, and confidence scores. The text string with the PII entities redacted is also returned.
 
 ## Service and data limits
 

@@ -18,23 +18,13 @@ Use this quickstart to create a Personally Identifiable Information (PII) detect
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
 * The [Visual Studio IDE](https://visualstudio.microsoft.com/vs/)
 
-
-
 ## Setting up
 
-[!INCLUDE [Create an Azure resource](../../../includes/create-resource.md)]
-
-
-
-[!INCLUDE [Get your key and endpoint](../../../includes/get-key-endpoint.md)]
-
-
-
 [!INCLUDE [Create environment variables](../../../includes/environment-variables.md)]
 
-
 ### Create a new .NET Core application
 
 Using the Visual Studio IDE, create a new .NET Core console app. This creates a "Hello World" project with a single C# source file: *program.cs*.

@@ -17,21 +17,11 @@ Use this quickstart to create a Personally Identifiable Information (PII) detect
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
 * [Java Development Kit (JDK)](https://www.oracle.com/technetwork/java/javase/downloads/index.html) with version 8 or above
 
-
-
-
 ## Setting up
 
-[!INCLUDE [Create an Azure resource](../../../includes/create-resource.md)]
-
-
-
-[!INCLUDE [Get your key and endpoint](../../../includes/get-key-endpoint.md)]
-
-
-
 [!INCLUDE [Create environment variables](../../../includes/environment-variables.md)]
 
 ### Add the client library

@@ -15,20 +15,11 @@ Use this quickstart to create a Personally Identifiable Information (PII) detect
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
 * [Node.js](https://nodejs.org/) v14 LTS or later
 
-
 ## Setting up
 
-
-[!INCLUDE [Create an Azure resource](../../../includes/create-resource.md)]
-
-
-
-[!INCLUDE [Get your key and endpoint](../../../includes/get-key-endpoint.md)]
-
-
-
 [!INCLUDE [Create environment variables](../../../includes/environment-variables.md)]
 
 ### Create a new Node.js application

@@ -15,20 +15,11 @@ Use this quickstart to create a Personally Identifiable Information (PII) detect
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
 * [Python 3.8 or later](https://www.python.org/)
 
-
 ## Setting up
 
-
-[!INCLUDE [Create an Azure resource](../../../includes/create-resource.md)]
-
-
-
-[!INCLUDE [Get your key and endpoint](../../../includes/get-key-endpoint.md)]
-
-
-
 [!INCLUDE [Create environment variables](../../../includes/environment-variables.md)]
 
 ### Install the client library

@@ -13,25 +13,15 @@ ms.custom: language-service-pii
 
 Use this quickstart to send Personally Identifiable Information (PII) detection requests using the REST API. In the following example, you will use cURL to identify [recognized sensitive information](../../concepts/entity-categories.md) in text.
 
-
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
-
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
 
 ## Setting up
 
-[!INCLUDE [Create an Azure resource](../../../includes/create-resource.md)]
-
-
-
-[!INCLUDE [Get your key and endpoint](../../../includes/get-key-endpoint.md)]
-
-
-
 [!INCLUDE [Create environment variables](../../../includes/environment-variables.md)]
 
-
 ## Create a JSON file with the example request body
 
 In a code editor, create a new file named `test_pii_payload.json` and copy the following JSON example. This example request will be sent to the API in the next step.

@@ -9,4 +9,4 @@ ms.custom: include
 ---
 
 > [!TIP]
-> You can use [**Language Studio**](../../language-studio.md) to try PII detection in documents without needing to write code.
+> You can use [**AI Studio**](../../../../ai-studio/what-is-ai-studio.md) to try summarization without needing to write code. 

@@ -19,6 +19,9 @@ Customers can now redact transcripts, chats, and other text written in a convers
 
 PII detection is one of the features offered by [Azure AI Language](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The PII detection feature can **identify, categorize, and redact** sensitive information in unstructured text. For example: phone numbers, email addresses, and forms of identification. Azure AI Language supports general text PII redaction, as well as [Conversational PII](how-to-call-for-conversations.md), a specialized model for handling speech transcriptions and the more informal, conversational tone of meeting and call transcripts. The service also supports [Native Document PII redaction](#native-document-support), where the input and output are structured document files.
 
+> [!TIP]
+> Try out PII detection [in AI Studio](https://ai.azure.com/explore/language), where you can [utilize a currently existing Language Studio resource or create a new AI Studio resource](../../../ai-studio/ai-services/connect-ai-services.md)
+
 * [**Quickstarts**](quickstart.md) are getting-started instructions to guide you through making requests to the service.
 * [**How-to guides**](how-to-call.md) contain instructions for using the service in more specific or customized ways.
 * The [**conceptual articles**](concepts/entity-categories.md) provide in-depth explanations of the service's functionality and features.

@@ -31,12 +31,11 @@ Use this quickstart to create a text summarization application with the client l
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * The [Visual Studio IDE](https://visualstudio.microsoft.com/vs/)
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint.  After it deploys, select **Go to resource**.
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
     * You'll need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code later in the quickstart.
     * You can use the free pricing tier (`Free F0`) to try the service, and upgrade later to a paid tier for production.
 * To use the Analyze feature, you'll need a Language resource with the standard (S) pricing tier.
 
-
 ## Setting up
 
 [!INCLUDE [Create environment variables](../../../includes/environment-variables.md)]

@@ -18,13 +18,11 @@ Use this quickstart to create a text summarization application with the client l
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * [Java Development Kit (JDK)](https://www.oracle.com/technetwork/java/javase/downloads/index.html) with version 8 or above
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint.  After it deploys, select **Go to resource**.
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
     * You'll need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code below later in the quickstart.
     * You can use the free pricing tier (`Free F0`) to try the service, and upgrade later to a paid tier for production.
 * To use the Analyze feature, you'll need a Language resource with the standard (S) pricing tier.
 
-
-
 ## Setting up
 
 ### Add the client library

@@ -10,18 +10,18 @@ ms.custom: devx-track-js
 
 [Reference documentation](/javascript/api/overview/azure/ai-language-text-readme?view=azure-node-latest&preserve-view=true) | [Additional samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/textanalytics/ai-text-analytics/samples) | [Package (npm)](https://www.npmjs.com/package/@azure/ai-text-analytics/v/5.2.0-beta.1) | [Library source code](https://github.com/Azure/azure-sdk-for-js/tree/master/sdk/textanalytics/ai-text-analytics) 
 
-Use this quickstart to create a text summarization application with the client library for Node.js. In the following example, you'll create a JavaScript application that can summarize documents.
+Use this quickstart to create a text summarization application with the client library for Node.js. In the following example, you create a JavaScript application that can summarize documents.
 
 [!INCLUDE [Use Language Studio](../use-language-studio.md)]
 
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * [Node.js](https://nodejs.org/) v16 LTS
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-    * You'll need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
+    * You need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code below later in the quickstart.
     * You can use the free pricing tier (`Free F0`) to try the service, and upgrade later to a paid tier for production.
-* To use the Analyze feature, you'll need a Language resource with the standard (S) pricing tier.
+* To use the Analyze feature, you need a Language resource with the standard (S) pricing tier.
 
 
 

@@ -31,7 +31,7 @@ Use this quickstart to create a text summarization application with the client l
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * [Python 3.x](https://www.python.org/)
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
     * You'll need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code below later in the quickstart.
     * You can use the free pricing tier (`Free F0`) to try the service, and upgrade later to a paid tier for production.
 * To use the Analyze feature, you'll need a Language resource with the standard (S) pricing tier.

@@ -25,7 +25,7 @@ Use this quickstart to send text summarization requests using the REST API. In t
 ## Prerequisites
 
 * The current version of [cURL](https://curl.haxx.se/).
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
+* Once you have your Azure subscription, <[create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
     * You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
     * You can use the free pricing tier (`Free F0`) to try the service, and upgrade later to a paid tier for production.
 

@@ -11,4 +11,4 @@ ms.custom: include, build-2024
 ---
 
 > [!TIP]
-> You can use [**Language Studio**](../../language-studio.md) to try text summarization without needing to write code. 
+> You can use [**AI Studio**](../../../../ai-studio/what-is-ai-studio.md) to try summarization without needing to write code. 

@@ -21,6 +21,9 @@ 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, and conversation summarization accept conversational input, including various speech audio signals in order for the model to effectively segment and summarize, and native document can directly summarize for documents in their native formats, such as Words, PDF, etc. 
 
+> [!TIP]
+> Try out Summarization [in AI Studio](https://ai.azure.com/explore/language), where you can [utilize a currently existing Language Studio resource or create a new AI Studio resource](../../../ai-studio/ai-services/connect-ai-services.md) in order to use this service. 
+
 # [Text summarization](#tab/text-summarization)
 
 This documentation contains the following article types:

@@ -0,0 +1,89 @@
+---
+title: Fast Healthcare Interoperability Resources (FHIR) structuring in Text Analytics for health
+titleSuffix: Azure AI services
+description: Learn about Fast Healthcare Interoperability Resources (FHIR) structuring
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: conceptual
+ms.date: 11/04/2024
+ms.author: jboback
+ms.custom: language-service-health
+---
+
+# Utilizing Fast Healthcare Interoperability Resources (FHIR) structuring in Text Analytics for Health
+
+When you process unstructured data using Text Analytics for health, you can request that the output response includes a Fast Healthcare Interoperability Resources (FHIR) resource bundle. The FHIR resource bundle output is enabled by passing the FHIR version as part of the options in each request. How you pass the FHIR version differs depending on whether you're using the SDK or the REST API.
+
+## Use the REST API
+When you use the REST API as part of building the request payload, you include a Tasks object. Each of the Tasks can have parameters. One of the options for parameters is `fhirVersion`. By including the `fhirVersion` parameter in the Task object parameters, you're requesting the output to include a FHIR resource bundle in addition to the normal Text Analytics for health output. The following example shows the inclusion of `fhirVersion` in the request parameters.
+
+```json
+{
+      "analysis input": {
+            "documents:"[
+                {
+                text:"54 year old patient had pain in the left elbow with no relief from 100 mg Ibuprofen",
+                "language":"en",
+                "id":"1"
+                }
+            ]
+        },
+    "tasks"[
+       {
+       "taskId":"analyze 1",
+       "kind":"Healthcare",
+       "parameters":
+            {
+            "fhirVersion":"4.0.1"
+            }
+        }
+    ]
+}
+```
+
+Once the request has completed processing by Text Analytics for health and you pull the response from the REST API, you'll find the FHIR resource bundle in the output. You can locate the FHIR resource bundle inside each document processed using the property name `fhirBundle`. The following partial sample is output highlighting the `fhirBundle`.
+
+```json
+{
+  "jobID":"50d11b05-7a03-a611-6f1e95ebde07",
+  "lastUpdatedDateTime":"2024-06-05T17:29:51Z",
+  "createdDateTime:"2024-06-05T17:29:40Z",
+  "expirationDateTime":"2024-06-05T17:29:40Z",
+  "status":"succeeded",
+  "errors":[],
+  "tasks":{
+    "completed": 1,
+    "failed": 0,
+    "inProgress": 0,
+    "total": 1,
+    "items": [
+        {
+          "kind":"HealthcareLROResults",
+          "lastUpdatedDateTime":"2024-06-05T17:29:51.5839858Z",
+          "status":"succeeded",
+          "results": {
+              "documents": [
+                  {
+                    "id": "1",
+                    "entities": [...
+                    ],
+                    "relations": [...
+                    ].
+                    "warnings":[],
+                    "fhirBundle": {
+                        "resourceType": "Bundle",
+                        "id": "b4d907ed-0334-4186-9e21-8ed4d79e709f",
+                        "meta": {
+                            "profile": [
+                                "http://hl7.org/fhir/4.0.1/StructureDefinition/Bundle"
+                                  ]
+                                },  
+```
+
+## Use the REST SDK
+You can also use the SDK to make the request for Text Analytics for health to include the FHIR resource bundle in the output. To accomplish this request with the SDK, you would create an instance of `AnalyzeHealthcareEntitiesOptions` and populate the `FhirVersion` property with the FHIR version. This options object is then passed to each `StartAnalyzeHealthcareEntitiesAsync` method call to configure the request to include a FHIR resource bundle in the output.
+
+## Next steps
+
+* [How to call the Text Analytics for health](../how-to/call-api.md)
\ No newline at end of file

@@ -7,9 +7,9 @@ ms.date: 12/19/2023
 ms.author: jboback
 ---
 
-[Reference documentation](/dotnet/api/azure.ai.textanalytics?preserve-view=true&view=azure-dotnet) | [Additional samples](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/textanalytics/Azure.AI.TextAnalytics/samples) | [Package (NuGet)](https://www.nuget.org/packages/Azure.AI.TextAnalytics/5.2.0) | [Library source code](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/textanalytics/Azure.AI.TextAnalytics)
+[Reference documentation](/dotnet/api/azure.ai.textanalytics?preserve-view=true&view=azure-dotnet) | [More samples](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/textanalytics/Azure.AI.TextAnalytics/samples) | [Package (NuGet)](https://www.nuget.org/packages/Azure.AI.TextAnalytics/5.2.0) | [Library source code](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/textanalytics/Azure.AI.TextAnalytics)
 
-Use this quickstart to create a Text Analytics for health application with the client library for .NET. In the following example, you will create a C# application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
+Use this quickstart to create a Text Analytics for health application with the client library for .NET. In the following example, you create a C# application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
 
 
 
@@ -18,29 +18,27 @@ Use this quickstart to create a Text Analytics for health application with the c
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * The [Visual Studio IDE](https://visualstudio.microsoft.com/vs/)
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint.  After it deploys, select **Go to resource**.
-    * You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
-    * You can use the free pricing tier (`Free F0`) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
+    * You need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code later in the quickstart.
+    * You can use the free pricing tier (`Free F0`) to try the service (providing 5,000 text records - 1,000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
 
 
 
 ## Setting up
 
 [!INCLUDE [Create environment variables](../../../includes/environment-variables.md)]
 
-
-
 ### Create a new .NET Core application
 
-Using the Visual Studio IDE, create a new .NET Core console app. This will create a "Hello World" project with a single C# source file: *program.cs*.
+Using the Visual Studio IDE, create a new .NET Core console app. This action creates a "Hello World" project with a single C# source file: *program.cs*.
 
 Install the client library by right-clicking on the solution in the **Solution Explorer** and selecting **Manage NuGet Packages**. In the package manager that opens select **Browse** and search for `Azure.AI.TextAnalytics`. Select version `5.2.0`, and then **Install**. You can also use the [Package Manager Console](/nuget/consume-packages/install-use-packages-powershell#find-and-install-a-package).
 
 
 
 ## Code example
 
-Copy the following code into your *program.cs* file. Then run the code.  
+Copy the following code into your *program.cs* file. Then run the code.
 
 [!INCLUDE [find the key and endpoint for a resource](../../../includes/find-azure-resource-info.md)]
 
@@ -180,4 +178,4 @@ Results of Azure Text Analytics for health async model, version: "2022-03-01"
 ```
 
 > [!TIP]
-> Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.
+> Fast Healthcare Interoperability Resources (FHIR) structuring is available using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.

@@ -8,17 +8,17 @@ ms.custom: devx-track-java
 ms.author: jboback
 ---
 
-[Reference documentation](/java/api/overview/azure/ai-textanalytics-readme?preserve-view=true&view=azure-java-stable) | [Additional samples](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/textanalytics/azure-ai-textanalytics/src/samples) | [Package (Maven)](https://mvnrepository.com/artifact/com.azure/azure-ai-textanalytics/5.2.0) | [Library source code](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/textanalytics/azure-ai-textanalytics)
+[Reference documentation](/java/api/overview/azure/ai-textanalytics-readme?preserve-view=true&view=azure-java-stable) | [More samples](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/textanalytics/azure-ai-textanalytics/src/samples) | [Package (Maven)](https://mvnrepository.com/artifact/com.azure/azure-ai-textanalytics/5.2.0) | [Library source code](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/textanalytics/azure-ai-textanalytics)
 
-Use this quickstart to create a Text Analytics for health application with the client library for Java. In the following example, you will create a Java application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
+Use this quickstart to create a Text Analytics for health application with the client library for Java. In the following example, you create a Java application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
 
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * [Java Development Kit (JDK)](https://www.oracle.com/technetwork/java/javase/downloads/index.html) with version 8 or above
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint.  After it deploys, select **Go to resource**.
-    * You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
-    * You can use the free pricing tier (`Free F0`) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
+    * You need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code later in the quickstart.
+    * You can use the free pricing tier (`Free F0`) to try the service (providing 5,000 text records - 1,000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5,000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
 
 
 
@@ -159,4 +159,4 @@ Relation type: FrequencyOfMedication.
 ```
 
 > [!TIP]
-> Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.
+> Fast Healthcare Interoperability Resources (FHIR) structuring is available using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.

@@ -8,18 +8,18 @@ ms.author: jboback
 ms.custom: devx-track-js
 ---
 
-[Reference documentation](/javascript/api/overview/azure/ai-language-text-readme) | [Additional samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/cognitivelanguage/ai-language-text/samples/v1) | [Package (npm)](https://www.npmjs.com/package/@azure/ai-language-text) | [Library source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/cognitivelanguage/ai-language-text) 
+[Reference documentation](/javascript/api/overview/azure/ai-language-text-readme) | [More samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/cognitivelanguage/ai-language-text/samples/v1) | [Package (npm)](https://www.npmjs.com/package/@azure/ai-language-text) | [Library source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/cognitivelanguage/ai-language-text) 
 
-Use this quickstart to create a Text Analytics for health application with the client library for Node.js. In the following example, you will create a JavaScript application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
+Use this quickstart to create a Text Analytics for health application with the client library for Node.js. In the following example, you create a JavaScript application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
 
 
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * [Node.js](https://nodejs.org/) v14 LTS or later
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-    * You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
-    * You can use the free pricing tier (`Free F0`) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
+    * You need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code later in the quickstart.
+    * You can use the free pricing tier (`Free F0`) to try the service (providing 5,000 text records - 1,000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5,000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
 
 
 
@@ -196,4 +196,4 @@ Last time the operation was updated was on: Mon Feb 13 2023 13:12:10 GMT-0800 (P
 ```
 
 > [!TIP]
-> Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.
+> Fast Healthcare Interoperability Resources (FHIR) structuring is available using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.

@@ -6,19 +6,19 @@ ms.date: 12/19/2023
 ms.author: jboback
 ---
 
-[Reference documentation](/python/api/azure-ai-textanalytics/azure.ai.textanalytics?preserve-view=true&view=azure-python) | [Additional samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/textanalytics/azure-ai-textanalytics/samples) | [Package (PyPi)](https://pypi.org/project/azure-ai-textanalytics/5.2.0/) | [Library source code](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/textanalytics/azure-ai-textanalytics) 
+[Reference documentation](/python/api/azure-ai-textanalytics/azure.ai.textanalytics?preserve-view=true&view=azure-python) | [More samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/textanalytics/azure-ai-textanalytics/samples) | [Package (PyPi)](https://pypi.org/project/azure-ai-textanalytics/5.2.0/) | [Library source code](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/textanalytics/azure-ai-textanalytics) 
 
 
-Use this quickstart to create a Text Analytics for health application with the client library for Python. In the following example, you will create a Python application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
+Use this quickstart to create a Text Analytics for health application with the client library for Python. In the following example, you create a Python application that can identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
 
 
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
 * [Python 3.8 or later](https://www.python.org/)
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-    * You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
-    * You can use the free pricing tier (`Free F0`) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
+    * You need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code later in the quickstart.
+    * You can use the free pricing tier (`Free F0`) to try the service (providing 5,000 text records - 1,000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5,000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
 
 
 
@@ -114,4 +114,4 @@ Relation of type: DosageOfMedication has the following roles
 ```
 
 > [!TIP]
-> Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.
+> Fast Healthcare Interoperability Resources (FHIR) structuring is available using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.

@@ -9,21 +9,21 @@ ms.author: jboback
 
 [Reference documentation](https://go.microsoft.com/fwlink/?linkid=2239169)
 
-Use this quickstart to send language detection requests using the REST API. In the following example, you will use cURL to identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
+Use this quickstart to send language detection requests using the REST API. In the following example, you use cURL to identify medical [entities](../../concepts/health-entity-categories.md), [relations](../../concepts/relation-extraction.md), and [assertions](../../concepts/assertion-detection.md) that appear in text.
 
 
 ## Prerequisites
 
 * The current version of [cURL](https://curl.haxx.se/)
 * An Azure subscription - [create one for free](https://azure.microsoft.com/free/cognitive-services/)
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">create a Language resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-    * You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
-    * You can use the free pricing tier (`Free F0`) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
+* Once you have your Azure subscription, [create an AI services resource](../../../../../ai-services/multi-service-resource.md?pivots=azportal#create-a-new-azure-ai-services-resource).
+    * You need the key and endpoint from the resource you create to connect your application to the API. You paste your key and endpoint into the code later in the quickstart.
+    * You can use the free pricing tier (`Free F0`) to try the service (providing 5,000 text records - 1,000 characters each) and upgrade later to the `Standard S` pricing tier for production. You can also start with the `Standard S` pricing tier, receiving the same initial quota for free (5,000 text records) before getting charged. For more information on pricing, visit [Language Service Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/).
 
 > [!NOTE]
 > * The following BASH examples use the `\` line continuation character. If your console or terminal uses a different line continuation character, use that character.
 > * You can find language specific samples on [GitHub](https://github.com/Azure-Samples/cognitive-services-quickstart-code).
-> * Go to the Azure portal and find the key and endpoint for the Language resource you created in the prerequisites. They will be located on the resource's **key and endpoint** page, under **resource management**. Then replace the strings in the code below with your key and endpoint.
+> * Go to the Azure portal and find the key and endpoint for the Language resource you created in the prerequisites. They are located on the resource's **key and endpoint** page, under **resource management**. Then replace the strings in the code below with your key and endpoint.
 To call the API, you need the following information:
 
 ## Setting up
@@ -52,4 +52,4 @@ The following cURL commands are executed from a BASH shell. Edit these commands
 
 
 > [!TIP]
-> Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.
+> Fast Healthcare Interoperability Resources (FHIR) structuring is available using the Language REST API. The client libraries are not currently supported. [Learn more](../../how-to/call-api.md) on how to use FHIR structuring in your API call.

@@ -18,6 +18,9 @@ ms.custom: language-service-health
 
 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 AI Studio](https://ai.azure.com/explore/language), where you can [utilize a currently existing Language Studio resource or create a new AI Studio resource](../../../ai-studio/ai-services/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.
 * The [**how-to guides**](how-to/call-api.md) contain detailed instructions on how to make calls to the service using the hosted API or using the on-premises Docker container.

@@ -99,6 +99,16 @@ items:
       href: conversational-language-understanding/faq.md
     - name: How-to guides
       items:
+        - name: Use containers
+          items:
+          - name: Use Docker Containers
+            href: conversational-language-understanding/how-to/use-containers.md
+          - name: Configure containers
+            href: concepts/configure-containers.md
+          - name: Use container instances
+            href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
+          - name: Azure AI containers overview
+            href: ../cognitive-services-container-support.md
         - name: Create projects
           href: conversational-language-understanding/how-to/create-project.md
         - name: Build a schema
@@ -286,7 +296,7 @@ items:
           href: named-entity-recognition/concepts/named-entity-categories.md
         - name: Entity Metadata
           href: named-entity-recognition/concepts/entity-metadata.md
-        - name: Preview API overview
+        - name: API version mapping
           href: named-entity-recognition/concepts/ga-preview-mapping.md
       - name: Tutorials
         items:
@@ -683,6 +693,8 @@ items:
           href: text-analytics-for-health/concepts/relation-extraction.md
         - name: Assertion detection
           href: text-analytics-for-health/concepts/assertion-detection.md
+        - name: Fast Healthcare Interoperability Resources (FHIR) structuring
+          href: text-analytics-for-health/concepts/fhir.md
     - name: Custom (preview)
       items:
       - name: Custom text analytics for health overview
@@ -801,13 +813,13 @@ items:
     href: tutorials/power-automate.md
   - name: Use language in prompt flow
     href: tutorials/prompt-flow.md
-  - name: 🆕 Native document support
+  - name: Native document support
     items:
-    - name: 🆕 Use native documents for language processing
+    - name: Use native documents for language processing
       href: native-document-support/use-native-documents.md
-    - name: 🆕 Create SAS tokens for storage containers
+    - name: Create SAS tokens for storage containers
       href: native-document-support/shared-access-signatures.md
-    - name: 🆕 Create a managed identity for storage containers
+    - name: Create a managed identity for storage containers
       href: native-document-support/managed-identities.md  
   - name: Scenario deep-dives
     items:

@@ -47,12 +47,17 @@
         },
         {
             "source_path_from_root": "/articles/ai-studio/tutorials/deploy-copilot-sdk.md",
-            "redirect_url": "/azure/ai-studio/tutorials/copilot-sdk-create-resources",
+            "redirect_url": "/azure/ai-studio/tutorials/copilot-sdk-build-rag",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/ai-studio/tutorials/copilot-sdk-evaluate-deploy.md",
+            "redirect_url": "/azure/ai-studio/tutorials/copilot-sdk-evaluate",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/articles/ai-studio/tutorials/deploy-copilot-ai-studio.md",
-            "redirect_url": "/azure/ai-studio/tutorials/copilot-sdk-create-resources",
+            "redirect_url": "/azure/ai-studio/tutorials/copilot-sdk-build-rag",
             "redirect_document_id": false
         },
         {
@@ -67,7 +72,7 @@
         },
         {
             "source_path_from_root": "/articles/ai-studio/how-to/models-foundation-azure-ai.md",
-            "redirect_url": "/azure/ai-studio/ai-services/connect-ai-services",
+            "redirect_url": "/azure/ai-studio/ai-services/how-to/connect-ai-services",
             "redirect_document_id": true
         },
         {
@@ -100,6 +105,11 @@
             "redirect_url": "/azure/ai-studio/how-to/model-catalog-overview",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/ai-studio/how-to/model-benchmarks.md",
+            "redirect_url": "/azure/ai-studio/concepts/model-benchmarks",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/articles/ai-studio/how-to/llmops-azure-devops-prompt-flow.md",
             "redirect_url": "/azure/machine-learning/prompt-flow/how-to-end-to-end-llmops-with-prompt-flow",
@@ -137,7 +147,42 @@
         },
         {
             "source_path_from_root": "/articles/ai-studio/ai-services/get-started.md",
-            "redirect_url": "/azure/ai-studio/ai-services/connect-ai-services",
+            "redirect_url": "/azure/ai-studio/ai-services/how-to/connect-ai-services",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-studio/ai-services/where-to-use-ai-services.md",
+            "redirect_url": "/azure/ai-studio/ai-services/how-to/connect-ai-services",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-studio/ai-services/connect-ai-services.md",
+            "redirect_url": "/azure/ai-studio/ai-services/how-to/connect-ai-services",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-studio/ai-services/connect-azure-openai.md",
+            "redirect_url": "/azure/ai-studio/ai-services/how-to/connect-azure-openai",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-studio/how-to/groundedness.md",
+            "redirect_url": "/azure/ai-studio/concepts/content-filtering",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-studio/how-to/prompt-shields.md",
+            "redirect_url": "/azure/ai-studio/concepts/content-filtering",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-studio/concepts/evaluation-improvement-strategies.md",
+            "redirect_url": "/azure/ai-studio/concepts/evaluation-approach-gen-ai",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-studio/quickstarts/content-safety.md",
+            "redirect_url": "/azure/ai-studio/concepts/content-filtering",
             "redirect_document_id": false
         }
     ]

@@ -1,70 +0,0 @@
----
-title: Connect AI services to your hub in Azure AI Studio
-titleSuffix: Azure AI Studio
-description: Learn how to use AI services connections to do more via Azure AI Studio, SDKs, and APIs.
-manager: nitinme
-ms.service: azure-ai-studio
-ms.custom:
-  - ignite-2023
-  - build-2024
-ms.topic: how-to
-ms.date: 8/20/2024
-ms.reviewer: eur
-ms.author: eur
-author: eric-urban
----
-
-# Connect AI services to your hub in Azure AI Studio
-
-You can try out AI services for free in Azure AI Studio via model catalog cards and playground experiences. This article describes how to use AI services connections to do more via Azure AI Studio, SDKs, and APIs. 
-
-After you create a hub with AI services, you can use the AI services connection via the AI Studio UI, APIs, and SDKs. For example, you can try out AI services via **Home** > **AI Services** in the AI Studio UI as shown here.
-
-:::image type="content" source="../media/ai-services/ai-services-home.png" alt-text="Screenshot of the AI Services page in Azure AI Studio." lightbox="../media/ai-services/ai-services-home.png":::
-
-## Create a hub
-
-You need a hub to connect to AI services in Azure AI Studio. When you create a hub, a connection to AI services is automatically created.
-
-[!INCLUDE [Create Azure AI Studio hub](../includes/create-hub.md)]
-
-## Connect to AI services
-
-Your hub is now created and you can connect to AI services. From the **Hub overview** page, you can see the AI services connection that was created with the hub.
-
-:::image type="content" source="../media/how-to/hubs/hub-connected-resources.png" alt-text="Screenshot of the hub's AI services connections." lightbox="../media/how-to/hubs/hub-connected-resources.png":::
-
-You can use the AI services connection via the AI Studio UI, APIs, and SDKs. 
-
-### Use the AI services connection in the AI Studio UI
-
-No further configuration is needed to use the AI services connection in the AI Studio UI. You can try out AI services via **Home** > **AI Services** in the AI Studio UI.
-
-Here are examples of more ways to use AI services in the AI Studio UI.
-
-- [Get started with assistants and code interpreters in the AI Studio playground](../../ai-services/openai/assistants-quickstart.md?context=/azure/ai-studio/context/context)
-- [Hear and speak with chat models in the AI Studio playground](../quickstarts/hear-speak-playground.md)
-- [Analyze images and videos using GPT-4 Turbo with Vision](../quickstarts/multimodal-vision.md)
-- [Use your image data with Azure OpenAI](../how-to/data-image-add.md)
-
-### Use the AI services connection in APIs and SDKs
-
-You can use the AI services connection via the APIs and SDKs for a subset of AI services: Azure OpenAI, Speech, Language, Translator, Vision, Document Intelligence, and Content Safety.
-
-To use the AI services connection via the APIs and SDKs, you need to get the key and endpoint for the connection.
-
-1. From the **Home** page in AI Studio, select **All hubs** from the left pane. Then select [the hub you created](#create-a-hub).
-1. Select the **AI Services** connection from the **Hub overview** page.
-1. You can find the key and endpoint for the AI services connection on the **Connection details** page.
-    
-    :::image type="content" source="../media/how-to/hubs/hub-connected-resource-key.png" alt-text="Screenshot of the AI services connection details." lightbox="../media/how-to/hubs/hub-connected-resource-key.png":::
-
-The AI services key and endpoint are used to authenticate and connect to AI services via the APIs and SDKs.
-
-For more information about AI services APIs and SDKs, see the [Azure AI services SDK reference documentation](../../ai-services/reference/sdk-package-resources.md?context=/azure/ai-studio/context/context) and [Azure AI services REST API](../../ai-services/reference/rest-api-resources.md?context=/azure/ai-studio/context/context) reference documentation.
-
-## Related content
-
-- [What are Azure AI services?](../../ai-services/what-are-ai-services.md?context=/azure/ai-studio/context/context)
-- [Azure AI Studio hubs](../concepts/ai-resources.md)
-- [Connections in Azure AI Studio](../concepts/connections.md)

@@ -0,0 +1,67 @@
+---
+title: Content Safety in Azure AI Studio overview
+titleSuffix: Azure AI Studio
+description: Learn how to use Azure AI Content Safety in Azure AI Studio to detect harmful user-generated and AI-generated content in applications and services.
+manager: nitinme
+ms.service: azure-ai-studio
+ms.custom:
+  - ignite-2024
+ms.topic: overview
+ms.date: 11/09/2024
+ms.author: pafarley
+author: PatrickFarley
+---
+
+# Content Safety in Azure AI Studio
+
+Azure AI Content Safety is an AI service that detects harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes various APIs that allow you to detect and prevent the output of harmful content. The interactive Content Safety **try out** page in AI Studio allows you to view, explore, and try out sample code for detecting harmful content across different modalities. 
+
+## Features 
+
+You can use Azure AI Content Safety for many scenarios: 
+
+**Text content**: 
+- Moderate text content: This feature scans and moderates text content, identifying and categorizing it based on different levels of severity to ensure appropriate responses. 
+- Groundedness detection: This filter determines if the AI's responses are based on trusted, user-provided sources, ensuring that the answers are "grounded" in the intended material. Groundedness detection is helpful for improving the reliability and factual accuracy of responses. 
+- Protected material detection for text: This feature identifies protected text material, such as known song lyrics, articles, or other content, ensuring that the AI doesn’t output this content without permission. 
+- Protected material detection for code: Detects code segments in the model's output that match known code from public repositories, helping to prevent uncredited or unauthorized reproduction of source code. 
+- Prompt shields: This feature provides a unified API to address "Jailbreak" and "Indirect Attacks": 
+    - Jailbreak Attacks: Attempts by users to manipulate the AI into bypassing its safety protocols or ethical guidelines. Examples include prompts designed to trick the AI into giving inappropriate responses or performing tasks it was programmed to avoid. 
+    - Indirect Attacks: Also known as Cross-Domain Prompt Injection Attacks, indirect attacks involve embedding malicious prompts within documents that the AI might process. For example, if a document contains hidden instructions, the AI might inadvertently follow them, leading to unintended or unsafe outputs. 
+
+**Image content**: 
+- Moderate image content: Similar to text moderation, this feature filters and assesses image content to detect inappropriate or harmful visuals. 
+- Moderate multimodal content: This is designed to handle a combination of text and images, assessing the overall context and any potential risks across multiple types of content. 
+
+**Customize your own categories**: 
+- Custom categories: Allows users to define specific categories for moderating and filtering content, tailoring safety protocols to unique needs. 
+- Safety system message: Provides a method for setting up a "System Message" to instruct the AI on desired behavior and limitations, reinforcing safety boundaries and helping prevent unwanted outputs. 
+
+## Understand harm categories
+
+### Harm categories
+
+| Category  | Description         |API term |
+| --------- | ------------------- | --- |
+| Hate and Fairness      | Hate and fairness harms refer to any content that attacks or uses discriminatory language with reference to a person or identity group based on certain differentiating attributes of these groups. <br><br>This includes, but is not limited to:<ul><li>Race, ethnicity, nationality</li><li>Gender identity groups and expression</li><li>Sexual orientation</li><li>Religion</li><li>Personal appearance and body size</li><li>Disability status</li><li>Harassment and bullying</li></ul> | `Hate` |
+| Sexual  | Sexual describes language related to anatomical organs and genitals, romantic relationships and sexual acts, acts portrayed in erotic or affectionate terms, including those portrayed as an assault or a forced sexual violent act against one’s will. <br><br> This includes but is not limited to:<ul><li>Vulgar content</li><li>Prostitution</li><li>Nudity and Pornography</li><li>Abuse</li><li>Child exploitation, child abuse, child grooming</li></ul>   | `Sexual` |
+| Violence  | Violence describes language related to physical actions intended to hurt, injure, damage, or kill someone or something; describes weapons, guns, and related entities. <br><br>This includes, but isn't limited to:  <ul><li>Weapons</li><li>Bullying and intimidation</li><li>Terrorist and violent extremism</li><li>Stalking</li></ul>  | `Violence` |
+| Self-Harm  | Self-harm describes language related to physical actions intended to purposely hurt, injure, damage one’s body or kill oneself. <br><br> This includes, but isn't limited to: <ul><li>Eating Disorders</li><li>Bullying and intimidation</li></ul>  | `SelfHarm` |
+
+### Severity levels 
+
+| Level | Description |
+| --- | ---|
+|Safe |Content might be related to violence, self-harm, sexual, or hate categories but the terms are used in general, journalistic, scientific, medical, and similar professional contexts, which are appropriate for most audiences. |
+|Low |Content that expresses prejudiced, judgmental, or opinionated views, includes offensive use of language, stereotyping, use cases exploring a fictional world (for example, gaming, literature) and depictions at low intensity.| 
+|Medium |Content that uses offensive, insulting, mocking, intimidating, or demeaning language towards specific identity groups, includes depictions of seeking and executing harmful instructions, fantasies, glorification, promotion of harm at medium intensity. |
+|High |Content that displays explicit and severe harmful instructions, actions, damage, or abuse; includes endorsement, glorification, or promotion of severe harmful acts, extreme or illegal forms of harm, radicalization, or nonconsensual power exchange or abuse. |
+
+## Limitations
+
+Refer to the [Content Safety overview](/azure/ai-services/content-safety/overview) for supported regions, rate limits, and input requirements for all features. Refer to the [Language support](/azure/ai-services/content-safety/language-support) page for supported languages. 
+
+
+## Next step 
+
+Get started using Azure AI Content Safety in Azure AI Studio by following the [How-to guide](./how-to/content-safety.md).
\ No newline at end of file

@@ -0,0 +1,158 @@
+---
+title: How to use Azure AI services in AI Studio
+titleSuffix: Azure AI Studio
+description: Learn how to use Azure AI services in AI Studio. You can use existing Azure AI services resources in AI Studio by creating a connection to the resource.
+manager: nitinme
+ms.service: azure-ai-studio
+ms.custom:
+  - ignite-2023
+  - build-2024
+  - ignite-2024
+ms.topic: how-to
+ms.date: 11/19/2024
+ms.reviewer: eur
+ms.author: eur
+author: eric-urban
+---
+
+# How to use Azure AI services in AI Studio
+
+You might have existing resources for Azure AI services that you used in the old studios such as Azure OpenAI Studio or Speech Studio. You can pick up where you left off by using your existing resources in AI Studio.
+
+This article describes how to use new or existing Azure AI services resources in an AI Studio project.
+
+## Usage scenarios
+
+Depending on the AI service and model you want to use, you can use them in AI Studio via:
+- [Bring your existing Azure AI services resources](#bring-your-existing-azure-ai-services-resources-into-a-project) into a project. You can use your existing Azure AI services resources in an AI Studio project by creating a connection to the resource.
+- The [model catalog](#discover-azure-ai-models-in-the-model-catalog). You don't need a project to browse and discover Azure AI models. Some of the Azure AI services are available for you to try via the model catalog without a project. Some Azure AI services require a project to use in the playgrounds.
+- The [project-level playgrounds](#try-azure-ai-services-in-the-project-level-playgrounds). You need a project to try Azure AI services such as Azure AI Speech and Azure AI Language. 
+- [Azure AI Services demo pages](#try-out-azure-ai-services-demos). You can browse Azure AI services capabilities and step through the demos. You can try some limited demos for free without a project.
+- [Fine-tune](#fine-tune-azure-ai-services-models) models. You can fine-tune a subset of Azure AI services models in AI Studio.
+- [Deploy](#deploy-models-to-production) models. You can deploy base models and fine-tuned models to production. Most Azure AI services models are already deployed and ready to use.
+
+## Bring your existing Azure AI services resources into a project
+
+Let's look at two ways to connect Azure AI services resources to a project:
+
+- [When you create a project](#connect-azure-ai-services-when-you-create-a-project-for-the-first-time)
+- [After you create a project](#connect-azure-ai-services-after-you-create-a-project)
+
+### Connect Azure AI services when you create a project for the first time
+
+When you create a project for the first time, you also create a hub. When you create a hub, you can select an existing Azure AI services resource (including Azure OpenAI) or create a new AI services resource.
+
+:::image type="content" source="../../media/how-to/projects/projects-create-resource.png" alt-text="Screenshot of the create resource page within the create project dialog." lightbox="../../media/how-to/projects/projects-create-resource.png":::
+
+For more details about creating a project, see the [create an AI Studio project](../../how-to/create-projects.md) how-to guide or the [create a project and use the chat playground](../../quickstarts/get-started-playground.md) quickstart.
+
+### Connect Azure AI services after you create a project
+
+To use your existing Azure AI services resources (such as Azure AI Speech) in an AI Studio project, you need to create a connection to the resource.
+
+1. Create an AI Studio project. For detailed instructions, see [Create an AI Studio project](../../how-to/create-projects.md).
+1. Go to your AI Studio project.
+1. Select **Management center** from the left pane.
+1. Select **Connected resources** (under **Project**) from the left pane. 
+1. Select **+ New connection**.
+
+    :::image type="content" source="../../media/ai-services/connections-add.png" alt-text="Screenshot of the connected resources page with the button to create a new connection." lightbox="../../media/ai-services/connections-add.png":::
+
+1. On the **Add a connection to external assets** page, select the kind of AI service that you want to connect to the project. For example, you can select Azure OpenAI Service, Azure AI Content Safety, Azure AI Speech, Azure AI Language, and other AI services.
+
+    :::image type="content" source="../../media/ai-services/connections-add-assets.png" alt-text="Screenshot of the page to select the kind of AI service that you want to connect to the project." lightbox="../../media/ai-services/connections-add-assets.png":::
+
+1. On the next page in the wizard, browse or search to find the resource you want to connect. Then select **Add connection**.  
+
+    :::image type="content" source="../../media/ai-services/connections-add-speech.png" alt-text="Screenshot of the page to select the Azure AI resource that you want to connect to the project." lightbox="../../media/ai-services/connections-add-speech.png":::
+
+1. After the resource is connected, select **Close** to return to the **Connected resources** page. You should see the new connection listed.
+
+## Discover Azure AI models in the model catalog
+
+You can discover Azure AI models in the model catalog without a project. Some Azure AI services are available for you to try via the model catalog without a project. 
+
+1. Go to the [AI Studio home page](https://ai.azure.com).
+1. Select the tile that says **Model catalog and benchmarks**. 
+
+    :::image type="content" source="../../media/explore/ai-studio-home-model-catalog.png" alt-text="Screenshot of the home page in Azure AI Studio with the option to select the model catalog tile." lightbox="../../media/explore/ai-studio-home-model-catalog.png":::
+
+    If you don't see this tile, you can also go directly to the [Azure AI model catalog page](https://ai.azure.com/explore/models) in AI Studio.
+
+1. From the **Collections** dropdown, select **Microsoft**. Search for Azure AI services models by entering **azure-ai** in the search box.
+
+    :::image type="content" source="../../media/ai-services/models/ai-services-model-catalog.png" alt-text="Screenshot of the model catalog page in Azure AI Studio with the option to search by collection and name." lightbox="../../media/ai-services/models/ai-services-model-catalog.png":::
+
+1. Select a model to view more details about it. You can also try the model if it's available for you to try without a project.
+
+## Try Azure AI services in the project level playgrounds
+
+In the project-level playgrounds, you can try Azure AI services such as Azure AI Speech and Azure AI Language. 
+
+1. Go to your AI Studio project. If you need to create a project, see [Create an AI Studio project](../../how-to/create-projects.md).
+1. Select **Playgrounds** from the left pane and then select a playground to use. In this example, select **Try the Speech playground**.
+
+    :::image type="content" source="../../media/ai-services/playgrounds/azure-ai-services-playgrounds.png" alt-text="Screenshot of the project level playgrounds that you can use." lightbox="../../media/ai-services/playgrounds/azure-ai-services-playgrounds.png":::
+
+1. Optionally, you can select a different connection to use in the playground. In the Speech playground, you can connect to Azure AI Services multi-service resources or Speech service resources. 
+
+    :::image type="content" source="../../media/ai-services/playgrounds/speech-playground.png" alt-text="Screenshot of the Speech playground in a project." lightbox="../../media/ai-services/playgrounds/speech-playground.png":::
+
+If you have other connected resources, you can use them in the corresponding playgrounds. For example, in the Language playground, you can connect to Azure AI Services multi-service resources or Azure AI Language resources.
+
+:::image type="content" source="../../media/ai-services/playgrounds/language-playground.png" alt-text="Screenshot of the Language playground in a project." lightbox="../../media/ai-services/playgrounds/language-playground.png":::
+
+## Try out Azure AI Services demos
+
+You can browse Azure AI services capabilities and step through the demos. You can try some limited demos for free without a project.
+
+1. Go to the [AI Studio home page](https://ai.azure.com) and make sure you're signed in with the Azure subscription that has your Azure AI services resource.
+1. Find the tile that says **Explore Azure AI Services** and select **Try now**. 
+
+    :::image type="content" source="../../media/explore/home-ai-services.png" alt-text="Screenshot of the home page in Azure AI Studio with the option to select Azure AI Services." lightbox="../../media/explore/home-ai-services.png":::
+
+    If you don't see this tile, you can also go directly to the [Azure AI Services page](https://ai.azure.com/explore/aiservices) in AI Studio.
+
+1. You should see tiles for Azure AI services that you can try. Select a tile to get to the demo page for that service. For example, select **Language + Translator**.
+
+    :::image type="content" source="../../media/ai-services/overview/ai-services-capabilities.png" alt-text="Screenshot of the landing page to try Azure AI Services try out capabilities in Azure AI Studio." lightbox="../../media/ai-services/overview/ai-services-capabilities.png":::
+
+The presentation and flow of the demo pages might vary depending on the service. In some cases, you need to select a project or connection to use the service. 
+
+## Fine-tune Azure AI services models
+
+In AI Studio, you can fine-tune some Azure AI services models. For example, you can fine-tune a model for custom speech. 
+
+1. Go to your AI Studio project. If you need to create a project, see [Create an AI Studio project](../../how-to/create-projects.md).
+1. Select **Fine-tuning** from the left pane.
+1. Select **AI Service fine-tuning**.
+
+    :::image type="content" source="../../media/ai-services/fine-tune-azure-ai-services.png" alt-text="Screenshot of the page to select fine-tuning of Azure AI Services models." lightbox="../../media/ai-services/fine-tune-azure-ai-services.png":::
+
+1. Select **+ Fine-tune**.
+1. Follow the wizard to fine-tune a model for the capabilities that you want.
+
+## Deploy models to production
+
+Once you have a project, several Azure AI services models are already deployed and ready to use. 
+
+1. Go to your AI Studio project.
+1. Select **Management center** from the left pane.
+1. Select **Models + endpoints** (under **Project**) from the left pane. 
+1. Select the **Service deployments** tab to view the list of Azure AI services models that are already deployed.
+
+    :::image type="content" source="../../media/ai-services/endpoint/models-endpoints-ai-services-deployments.png" alt-text="Screenshot of the models and endpoints page to view Azure AI services deployments." lightbox="../../media/ai-services/endpoint/models-endpoints-ai-services-deployments.png":::
+
+    In this example, we see:
+    - Six Azure AI Services deployments (such as Azure AI Speech and Azure AI Language) via the default connection. These models were already available for use when you created the project.
+    - Another Azure AI Speech deployment via the `contosoazureaispeecheastus` example connection. This example assumes that you connected to an Azure AI Speech resource after creating the project. For more information about connecting to Azure AI services, see [Use your existing Azure OpenAI and AI services resources](./connect-ai-services.md).
+
+There's no option to deploy Azure AI services models from the **Models + endpoints** page. Azure AI services models are already deployed and ready to use.
+
+However, you can deploy [fine-tuned Azure AI services models](#fine-tune-azure-ai-services-models). For example, you might want to deploy a custom speech model that you fine-tuned. In this case, you can deploy the model from the corresponding fine-tuning page. 
+
+
+## Related content
+
+- [What are Azure AI services?](../../../ai-services/what-are-ai-services.md?context=/azure/ai-studio/context/context)
+- [Connections in Azure AI Studio](../../concepts/connections.md)