@@ -51,7 +51,7 @@ Creating a CLU project typically involves several different steps.
 :::image type="content" source="media/llm-quick-deploy.png" alt-text="Chart of the LLM-powered quick deploy path." lightbox="media/llm-quick-deploy.png":::
 
 > [!NOTE]
-> In the Azure AI Foundry, you’ll create a fine-tuning task as your workspace for customizing your CLU model. Formerly, a CLU fine-tuning task was called a CLU project. You may see these terms used interchangeably in legacy CLU documentation.
+> In the Azure AI Foundry, you'll create a fine-tuning task as your workspace for customizing your CLU model. Formerly, a CLU fine-tuning task was called a CLU project. You may see these terms used interchangeably in legacy CLU documentation.
 
 CLU offers two paths for you to get the most out of your implementation.
 
@@ -61,7 +61,7 @@ Option 1 (LLM-powered quick deploy):
 
 2. **Deploy the model**: Deploying a model with the LLM-based training config makes it available for use via the Runtime API.
 
-3. **Predict intents and entities**: Use your custom model deployment to predict custom intents and prebuilt entities from user’s utterances. 
+3. **Predict intents and entities**: Use your custom model deployment to predict custom intents and prebuilt entities from user's utterances. 
 
 Option 2 (Custom machine learned model)
 
@@ -94,7 +94,7 @@ As you use CLU, see the following reference documentation and samples for Azure
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who use it, the people who are affected by it, and the environment in which it's deployed. Read the transparency note for CLU to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes the technology, the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the transparency note for CLU to learn about responsible AI use and deployment in your systems. 
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

@@ -76,7 +76,7 @@ As you use custom text classification, see the following reference documentation
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for custom text classification](/azure/ai-foundry/responsible-ai/language-service/custom-text-classification-transparency-note) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for custom text classification](/azure/ai-foundry/responsible-ai/language-service/custom-text-classification-transparency-note) to learn about responsible AI use and deployment in your systems. 
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

@@ -28,7 +28,7 @@ This documentation contains the following types of articles:
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for entity linking](/azure/ai-foundry/responsible-ai/language-service/transparency-note) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for entity linking](/azure/ai-foundry/responsible-ai/language-service/transparency-note) to learn about responsible AI use and deployment in your systems. 
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

@@ -6,14 +6,14 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 02/17/2025
+ms.date: 08/19/2025
 ms.author: lajanuar
 ms.custom: language-service-key-phrase
 ---
 
 # What is key phrase extraction in Azure AI Language?
 
-Key phrase extraction 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. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "*The food was delicious and the staff were wonderful.*", key phrase extraction returns the main topics: "*food*" and "*wonderful staff*."
+Key phrase extraction is one of the features offered by [Azure AI Language](../overview.md). This capability is part of a suite of cloud-based machine learning and AI tools designed for building intelligent applications that process written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "*The food was delicious and the staff were wonderful.*", key phrase extraction returns the main topics: "*food*" and "*wonderful staff*."
 
 This documentation contains the following types of articles:
 
@@ -31,7 +31,7 @@ This documentation contains the following types of articles:
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who use it, the people who are affected by it, and the environment in which it's deployed. Read the [transparency note for key phrase extraction](/azure/ai-foundry/responsible-ai/language-service/transparency-note-key-phrase-extraction) to learn about responsible AI use and deployment in your systems. For more information, see the following articles:
+An AI system includes the technology, the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the [transparency note for key phrase extraction](/azure/ai-foundry/responsible-ai/language-service/transparency-note-key-phrase-extraction) to learn about responsible AI use and deployment in your systems. For more information, see the following articles:
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 02/17/2025
+ms.date: 08/20/2025
 ms.author: lajanuar
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python

@@ -6,23 +6,22 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 02/17/2025
+ms.date: 08/19/2025
 ms.author: lajanuar
 ms.custom: language-service-language-detection
 ---
 
 # What is language detection in Azure AI Language?
 
-Language 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. Language detection is able to detect more than 100 languages in their primary script. In addition, it offers [script detection](./how-to/call-api.md#script-name-and-script-code) to detect supported scripts for each detected language according to the [ISO 15924 standard](https://wikipedia.org/wiki/ISO_15924) for a [select number of languages](./language-support.md#script-detection) supported by Azure AI Language Service.
-
+Language 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. Language detection is able to detect more than 100 languages in their primary script. In addition, the service offers [script detection](./how-to/call-api.md#script-name-and-script-code) for each detected language using  [ISO 15924 standard](https://wikipedia.org/wiki/ISO_15924) for a [select number of languages](./language-support.md#script-detection).
 This documentation contains the following types of articles:
 
 * [**Quickstarts**](quickstart.md) are getting-started instructions to guide you through making requests to the service.
 * [**How-to guides**](how-to/call-api.md) contain instructions for using the service in more specific or customized ways.
 
 ## Language detection features
 
-* Language detection: Returns one predominant language for each document you submit, along with its ISO 639-1 name, a human-readable name, confidence score, script name and script code according to ISO 15924 standard.
+* Language detection: For each document, returns the main language, its ISO 639-1 code, readable name, confidence score, script name, and ISO 15924 script code.
 
 * Script detection: To distinguish between multiple scripts used to write certain languages, such as Kazakh, language detection returns a script name and script code according to the ISO 15924 standard.  
 
@@ -37,7 +36,7 @@ This documentation contains the following types of articles:
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it's deployed. Read the [transparency note for language detection](/azure/ai-foundry/responsible-ai/language-service/transparency-note-language-detection) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also individuals who operate the system, people who experience its effects, and the broader environment where the system functions. Read the [transparency note for language detection](/azure/ai-foundry/responsible-ai/language-service/transparency-note-language-detection) to learn about responsible AI use and deployment in your systems. 
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 02/15/2025
+ms.date: 08/20/2025
 ms.author: lajanuar
 ms.custom: language-service-ner, ignite-2024
 ---
@@ -24,7 +24,7 @@ The NER feature can evaluate unstructured text, and extract named entities from
 
 ### Input languages
 
-When you submit input text 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, key phrase extraction defaults 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 input text 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, key phrase extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md). 
 
 ## Submitting data
 
@@ -86,7 +86,7 @@ The above examples would return entities falling under the `Location` entity typ
     
 ```
 
-This method returns all `Location` entities only falling under the `GPE` tag and ignore any other entity falling under the `Location` type that is tagged with any other entity tag such as `Structural` or `Geological` tagged `Location` entities. We could also further drill down on our results by using the `excludeList` parameter. `GPE` tagged entities could be tagged with the following tags: `City`, `State`, `CountryRegion`, `Continent`. We could, for example, exclude `Continent` and `CountryRegion` tags for our example:
+This method returns all `Location` entities only falling under the `GPE` tag and ignore any other entity falling under the `Location` type that is tagged with any other entity tag such as `Structural` or `Geological` tagged `Location` entities. We can also further analyze our results by using the `excludeList` parameter. `GPE` tagged entities could be tagged with the following tags: `City`, `State`, `CountryRegion`, `Continent`. We could, for example, exclude `Continent` and `CountryRegion` tags for our example:
 
 ```bash
 
@@ -107,19 +107,19 @@ 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
+## Supported 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 used to filter in the `inclusionList` and `exclusionList` parameters.
+|`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 `City`. The type is `City` for the text "Seattle."|
+|`tags`               |List (tags) |A list of tag objects that expresses the affinity of the detected entity to a hierarchy or any other grouping.<br><br>A tag contains two fields:<br>- `name`: A unique name for the tag.<br>- `confidenceScore`: The associated confidence score for a tag ranging from 0 to 1.<br><br>This unique tagName is 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.
+This sample output includes an example of output attributes.
 
 ```bash
 { 

@@ -8,7 +8,7 @@ ms.service: azure-ai-language
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 02/19/2025
+ms.date: 08/20/2025
 ms.author: lajanuar
 ---
 

@@ -69,7 +69,7 @@ As you use orchestration workflow, see the following reference documentation and
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the transparency note for CLU and orchestration workflow to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the transparency note for CLU and orchestration workflow to learn about responsible AI use and deployment in your systems. 
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

@@ -6,20 +6,20 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 02/17/2025
+ms.date: 08/19/2025
 ms.author: lajanuar
 ms.custom: language-service-sentiment-opinion-mining
 ---
 
 # What is sentiment analysis and opinion mining?
 
-Sentiment analysis and opinion mining are features offered by [the Language service](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. These features help you find out what people think of your brand or topic by mining text for clues about positive or negative sentiment, and can associate them with specific aspects of the text. 
+Sentiment analysis and opinion mining are features offered by [the Language service](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. These features help you discover what people think about your brand or topic by analyzing text for signs of positive or negative sentiment. They can also link these sentiments to specific aspects of the text.
 
 Both sentiment analysis and opinion mining work with various [written languages](./language-support.md).
 
-## Sentiment analysis 
+## Sentiment analysis
 
-The sentiment analysis feature provides sentiment labels (such as "negative", "neutral" and "positive") based on the highest confidence score found by the service at a sentence and document-level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral, and negative sentiment. 
+The sentiment analysis feature assigns sentiment labels, such as "negative," "neutral," and "positive." The service determines these labels using the highest confidence score. Sentiment is evaluated at both the sentence level and the document level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral, and negative sentiment.
 
 ## Opinion mining
 
@@ -31,7 +31,7 @@ Opinion mining is a feature of sentiment analysis, also known as aspect-based se
 
 [!INCLUDE [development options](./includes/development-options.md)]
 
-[!INCLUDE [Developer reference](../includes/reference-samples-text-analytics.md)] 
+[!INCLUDE [Developer reference](../includes/reference-samples-text-analytics.md)]
 
 ## Reference documentation
 
@@ -42,13 +42,12 @@ As you use sentiment analysis, see the following reference documentation and sam
 |REST APIs (Authoring)   | [REST API documentation](https://aka.ms/ct-authoring-swagger)        |         |
 |REST APIs (Runtime)    | [REST API documentation](https://aka.ms/ct-runtime-swagger)        |         |
 
---- 
+---
 
-## Responsible AI 
+## Responsible AI
 
-An AI system includes not only the technology, but also the people who use it, the people who are affected by it, and the environment in which it's deployed. Read the [transparency note for sentiment analysis](/azure/ai-foundry/responsible-ai/language-service/transparency-note-sentiment-analysis) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system encompasses more than just the technology itself. An AI system includes the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the [transparency note for sentiment analysis](/azure/ai-foundry/responsible-ai/language-service/transparency-note-sentiment-analysis) to learn about responsible AI use and deployment in your systems. 
 
 ## Next steps
 
-* The quickstart articles with instructions on using the service for the first time.
-    * [Use sentiment analysis and opinion mining](./quickstart.md)
\ No newline at end of file
+Get started with our quickstart articles with instructions on using the service for the first time: [Use sentiment analysis and opinion mining](./quickstart.md)
\ No newline at end of file

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 02/17/2025
+ms.date: 08/20/2025
 ms.author: lajanuar
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python

@@ -7,19 +7,19 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 02/17/2025
+ms.date: 08/19/2025
 ms.author: lajanuar
-ms.custom: language-service-health, ignite-2024
+ms.custom: language-service-health
 ---
 
 # What is Text Analytics for health?
 
 [!INCLUDE [service notice](includes/service-notice.md)]
 
-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. 
+Text Analytics for health is one of the prebuilt features offered by [Azure AI Language](../overview.md). Text Analytics for health uses machine learning to identify and label medical information in unstructured text such as doctor's notes, clinical documents, and electronic health records. It extracts key data from sources like discharge summaries to support healthcare analysis.
 
 > [!TIP]
-> Try out Text Analytics for health [in Azure AI Foundry portal](https://ai.azure.com/explore/language), where you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-services/connect-services-ai-foundry-portal.md) in order to use this service. 
+> Try out Text Analytics for health [in Azure AI Foundry portal](https://ai.azure.com/explore/language). There you can [utilize a currently existing Language Studio resource or create a new Azure AI Foundry resource](../../../ai-services/connect-services-ai-foundry-portal.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.
@@ -28,26 +28,31 @@ This documentation contains the following types of articles:
 
 ## Text Analytics for health features
 
-Text Analytics for health performs four key functions which are named entity recognition, relation extraction, entity linking, and assertion detection, all with a single API call.
+Text Analytics for health performs four key functions, all with a single API call:
+
+* Named entity recognition
+* Relation extraction
+* Entity linking
+* Assertion detection
 
 [!INCLUDE [Text Analytics for health](includes/features.md)]
 
 Text Analytics for health can receive unstructured text in English, German, French, Italian, Spanish, Portuguese, and Hebrew.
 
-Additionally, Text Analytics for health can return the processed output using the Fast Healthcare Interoperability Resources (FHIR) structure which enables the service's integration with other electronic health systems.  
+Additionally, Text Analytics for health can return the processed output using the Fast Healthcare Interoperability Resources (FHIR) structure that enables the service's integration with other electronic health systems.
 
 
 
 > [!VIDEO https://learn.microsoft.com/Shows/AI-Show/Introducing-Text-Analytics-for-Health/player]
 
 ## Usage scenarios
 
-Text Analytics for health can be used in multiple scenarios across a variety of industries.
+Text Analytics for health can be used in multiple scenarios across various industries.
 Some common customer motivations for using Text Analytics for health include:
 * Assisting and automating the processing of medical documents by proper medical coding to ensure accurate care and billing.
 * Increasing the efficiency of analyzing healthcare data to help drive the success of value-based care models similar to Medicare.
 * Minimizing healthcare provider effort by automating the aggregation of key patient data for trend and pattern monitoring.
-* Facilitating and supporting the adoption of HL7 standards for improved exchange, integration, sharing, retrieval, and delivery of electronic health information in all healthcare services.    
+* Facilitating and supporting the adoption of HL7 standards across the healthcare industry. By doing so, we help improve the exchange, integration, sharing, retrieval, and delivery of electronic health information in all areas of healthcare services.
 
 ### Example use cases: 
 
@@ -56,25 +61,25 @@ Some common customer motivations for using Text Analytics for health include:
 |Extract insights and statistics|Identify medical entities such as symptoms, medications, diagnosis from clinical and research documents in order to extract insights and statistics for different patient cohorts.|
 |Develop predictive models using historic data|Power solutions for planning, decision support, risk analysis and more, based on prediction models created from historic data.|
 |Annotate and curate medical information|Support solutions for clinical data annotation and curation such as automating clinical coding and digitizing manually created data.|
-|Review and report medical information|Support solutions for reporting and flagging possible errors in medical information resulting from reviewal processes such as quality assurance.|
-|Assist with decision support|Enable solutions that provide humans with assistive information relating to patients’ medical information for faster and more reliable decisions.|
+|Review and report medical information|potential medical information errors found during quality assurance reviews.|
+|Assist with decision support|Enable solutions that provide humans with assistive information relating to patients' medical information for faster and more reliable decisions.|
 
 ## Get started with Text Analytics for health
 
-[!INCLUDE [Development options](./includes/development-options.md)] 
+[!INCLUDE [Development options](./includes/development-options.md)]
 
 
 ## Input requirements and service limits
 
 Text Analytics for health is designed to receive unstructured text for analysis. For more information, see [data and service limits](../concepts/data-limits.md).
 
-Text Analytics for health works with a variety of input languages. For more information,  see [language support](language-support.md).
+Text Analytics for health works with various input languages. For more information,  see [language support](language-support.md).
 
-[!INCLUDE [Developer reference](../includes/reference-samples-text-analytics.md)] 
+[!INCLUDE [Developer reference](../includes/reference-samples-text-analytics.md)]
 
 
-## Responsible use of AI 
+## Responsible use of AI
 
-An AI system includes the technology, the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for Text Analytics for health](/azure/ai-foundry/responsible-ai/language-service/transparency-note-health) to learn about responsible AI use and deployment in your systems. You can also refer to the following articles for more information:
+An AI system includes the technology, the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the [transparency note for Text Analytics for health](/azure/ai-foundry/responsible-ai/language-service/transparency-note-health) to learn about responsible AI use and deployment in your systems. 
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]

@@ -7,7 +7,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 02/16/2025
+ms.date: 08/20/2025
 ms.author: lajanuar
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python
@@ -18,7 +18,7 @@ zone_pivot_groups: programming-languages-text-analytics
 
 # Quickstart: Using Text Analytics for health client library and REST API
 
-This article contains Text Analytics for health quickstarts that help with using the supported client libraries, C#, Java, NodeJS, and Python as well as with using the REST API.
+This article contains Text Analytics for health quickstarts that help with using the supported client libraries, C#, Java, NodeJS, and Python and the REST API.
 
 [!INCLUDE [Use Language Studio](../includes/use-language-studio.md)]
 ::: zone pivot="programming-language-csharp"