@@ -1,75 +0,0 @@
----
-title: Native document support for Azure Language in Foundry Tools (preview)
-titleSuffix: Foundry Tools
-description: How to use native document with Azure Language Personally Identifiable Information and Summarization capabilities.
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.custom:
-  - ignite-2024
-ms.topic: how-to
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-<!-- markdownlint-disable MD033 -->
-<!-- markdownlint-disable MD051 -->
-<!-- markdownlint-disable MD024 -->
-<!-- markdownlint-disable MD036 -->
-<!-- markdownlint-disable MD049 -->
-<!-- markdownlint-disable MD001 -->
-
-# Native document support for Azure Language in Foundry Tools (preview)
-
-> [!IMPORTANT]
->
-> * Azure Language in Foundry Tools public preview releases provide early access to features that are in active development.
-> * Features, approaches, and processes can change, before General Availability (GA), based on user feedback.
-
-Language is a cloud-based service that applies Natural Language Processing (NLP) features to text-based data. The native document support capability enables you to send API requests asynchronously, using an HTTP POST request body to send your data and HTTP GET request query string to retrieve the status results. Your processed documents are located in your Azure Blob Storage target container.
-
-A native document refers to the file format used to create the original document such as Microsoft Word (docx) or a portable document file (pdf). Native document support eliminates the need for text preprocessing before using Language resource capabilities. Currently, native document support is available for the following capabilities:
-
-* [Personally Identifiable Information (PII)](../personally-identifiable-information/overview.md). The PII detection feature can identify, categorize, and redact sensitive information in unstructured text. The `PiiEntityRecognition` API supports native document processing.
-
-* [Document summarization](../summarization/overview.md). Document summarization uses natural language processing to generate extractive (salient sentence extraction) or abstractive (contextual word extraction) summaries for documents. Both `AbstractiveSummarization` and `ExtractiveSummarization` APIs support native document processing.
-
-## Supported document formats
-
- Applications use native file formats to create, save, or open native documents. Currently **PII** and **Document summarization** capabilities supports the following native document formats:
-
-|File type|File extension|Description|
-|---------|--------------|-----------|
-|Text| `.txt`|An unformatted text document.|
-|Adobe PDF| `.pdf`|A portable document file formatted document.|
-|Microsoft Word| `.docx`|A Microsoft Word document file.|
-
-## Input guidelines
-
-***Supported file formats***
-
-|Type|support and limitations|
-|---|---|
-|**PDFs**| Fully scanned PDFs aren't supported.|
-|**Text within images**| Digital images with embedded text aren't supported.|
-|**Digital tables**| Tables in scanned documents aren't supported.|
-
-***Document Size***
-
-|Attribute|Input limit|
-|---|---|
-|**Total number of documents per request** |**≤ 20**|
-|**Total content size per request**| **≤ 10 MB**|
-
-## Request headers and parameters
-
-|parameter  |Description  |
-|---------|---------|
-|`-X POST <endpoint>`     | Specifies your Language resource endpoint for accessing the API.        |
-|`--header Content-Type: application/json`     | The content type for sending JSON data.          |
-|`--header "Ocp-Apim-Subscription-Key:<key>`    | Specifies Azure Language resource key for accessing the API.        |
-|`-data`     | The JSON file containing the data you want to pass with your request.         |
-
-## Related content
-
-> [!div class="nextstepaction"]
-> [PII detection overview](../personally-identifiable-information/overview.md "Learn more about Personally Identifiable Information detection.") [Document Summarization overview](../summarization/overview.md "Learn more about automatic document summarization.")

@@ -0,0 +1,91 @@
+---
+title: Conversation Personally Identifiable Information (PII) redaction overview
+titleSuffix: Foundry Tools
+description: Learn how conversation PII redaction in Azure Language detects and redacts sensitive data in turn-based conversational inputs.
+author: laujan
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: overview
+ms.date: 04/16/2026
+ms.author: lajanuar
+ms.custom: language-service-pii
+---
+
+<!-- markdownlint-disable MD025 -->
+# Conversation Personally Identifiable Information (PII) redaction overview
+
+Conversation PII redaction in Azure AI Language helps you detect and redact sensitive data in turn-based conversational input. You can use this feature for chat and transcript workflows such as customer support conversations, call transcripts, and meeting transcripts.
+
+Conversation PII is optimized for asynchronous conversation jobs and conversation-level context, so you can redact sensitive data across multiple speakers and turns.
+
+## At a glance
+
+Conversation PII provides the following capabilities:
+
+* Redaction for multi-turn conversation structures.
+* Asynchronous processing for transcript-style workloads.
+* Conversation-level handling across speakers and turns.
+* Structured entity output with redacted conversation content.
+
+## Why use conversation PII?
+
+Conversation PII is optimized for turn-based dialogue structures and supports conversational context that differs from single-block text inputs.
+
+Conversation PII is especially useful when you need to:
+
+* Redact sensitive entities in multi-turn conversation data.
+* Process transcript inputs from call center and speech workflows.
+* Preserve conversation-level context while masking sensitive information.
+
+## What it returns
+
+When a job succeeds, the service returns:
+
+* Detected entities with categories, subcategories, and confidence scores.
+* Redacted conversation output for conversation items.
+* Optional transcript-oriented metadata for supported transcript workflows.
+
+## How it works
+
+Conversation PII uses an asynchronous workflow:
+
+1. Submit conversation items as a conversation job.
+2. Poll the job status.
+3. Retrieve entities and redacted conversation output.
+
+For implementation details and request samples, see [Detect and redact Personally Identifiable Information in conversations](how-to/redact-conversation-pii.md).
+
+## How it differs from other PII feature types
+
+All PII feature types use predefined entity categories, but they optimize for different input types:
+
+* Conversation PII is optimized for turn-based and transcript-oriented conversational input.
+* Text PII is optimized for direct string-based input and app integration.
+* Document-based PII is optimized for native-file workflows and file output fidelity.
+
+## Common use cases
+
+Conversation PII is useful when teams need to anonymize conversational data before storage, analytics, quality review, external sharing, or downstream AI processing.
+
+Typical examples include:
+
+* Call center transcripts and quality-assurance workflows.
+* Customer support chat logs.
+* Meeting transcripts and collaboration records.
+* Multi-turn conversational datasets for analytics and model evaluation.
+
+## Supported formats and limits
+
+* Input format: conversation items (chat or transcript structures)
+* See [language support](language-support.md) and [quotas and limits](../concepts/data-limits.md) for current support details.
+
+## Pricing
+
+Conversation PII redaction uses Azure AI Language pricing. For current pricing details, see [Azure AI Language pricing](https://aka.ms/unifiedLanguagePricing).
+
+## Next steps
+
+Use the following references to continue implementation:
+
+* [Detect and redact Personally Identifiable Information in conversations](how-to/redact-conversation-pii.md)
+* [PII feature overview](overview.md)

@@ -0,0 +1,127 @@
+---
+title: Document-based PII overview
+titleSuffix: Foundry Tools
+description: Learn how document-based PII redaction in Azure Language detects and redacts sensitive data from native documents while preserving file structure.
+author: laujan
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: overview
+ms.date: 04/16/2026
+ms.author: lajanuar
+ms.custom: language-service-pii
+---
+
+<!-- markdownlint-disable MD025 -->
+# Document-based PII overview
+
+[**Document-based PII**](document-based-pii-overview.md) is a preview feature in Azure AI Language Personally Identifiable Information (PII) detection. It helps you detect and redact sensitive data directly in native document files, including Microsoft Word and PDF files, without building your own text extraction and reconstruction pipeline.
+
+This feature uses an asynchronous API workflow and returns redacted output that preserves document structure and formatting. You can use it when document fidelity is important for compliance review, sharing, analytics, and downstream AI workflows.
+
+> [!IMPORTANT]
+> Document-based PII is currently in preview and may change before general availability (GA).
+
+## At a glance
+
+Document-based PII provides the following capabilities:
+
+* Native document redaction for `.pdf`, `.docx`, and `.txt` files.
+* Preserved layout in output documents, including font, spacing, and color.
+* A single asynchronous API workflow for extraction, detection, and redaction.
+* Enterprise-ready outputs: a redacted document and a structured JSON result.
+
+## Video demonstration
+
+In this video, we introduce the PII detection service and show you how it detects and redacts sensitive data directly from native documents while preserving file structure and formatting. We also cover common use cases, supported formats, and how to get started with document-based PII in Azure AI Language:
+
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=ec4b7c3d-c2ff-45c6-ba4f-a816b0e5d7ce]
+
+Closed captions are available for this video.
+
+## Why use document-based PII?
+
+Many custom pipelines require multiple steps to extract text, run detection, and reconstruct document output. Document-based PII simplifies this flow with a single asynchronous API pattern and output artifacts designed for document-processing systems.
+
+Document-based PII is especially useful when you need to:
+
+* Redact PII in `.pdf`, `.docx`, and `.txt` files.
+* Preserve document layout for downstream business processes.
+* Generate structured JSON output for auditing and integration.
+
+Document-based PII uses the same predefined PII categories as text PII, including entities such as addresses, phone numbers, and credit card numbers.
+
+## What it returns
+
+When a job succeeds, you receive:
+
+* A redacted document in your target storage container.
+* A JSON result file with detected entities, categories, confidence scores, and processing metadata.
+
+## How it works
+
+Document-based PII uses an asynchronous workflow:
+
+1. Submit a job with source and target storage locations.
+1. Poll the job status by using the operation location.
+1. Retrieve output artifacts from your target storage location.
+
+:::image type="content" source="media/document-pii-workflow.png" alt-text="Diagram showing the asynchronous workflow for document-based PII detection.":::
+
+For implementation details and request samples, see [Detect and redact Personally Identifiable Information in native documents](how-to/redact-document-pii.md).
+
+## How it differs from other PII feature types
+
+All PII feature types use predefined entity categories, but they optimize for different input types:
+
+* Document-based PII is optimized for native-file redaction workflows and file output fidelity.
+* Text PII is optimized for direct string-based input and app integration.
+* Conversation PII is optimized for turn-based and transcript-oriented conversational input.
+
+## Common use cases
+
+Document-based PII is designed for enterprise and regulated-industry workflows where teams need to anonymize files before storage, analytics, external sharing, or downstream AI processing.
+
+Typical examples include:
+
+* Court records and legal documentation.
+* Government forms and internal records.
+* Financial documents.
+* Internal enterprise documentation workflows.
+
+## Supported formats and limits
+
+Document-based PII accepts native file formats directly, without requiring text preprocessing. The following table lists the supported formats:
+
+| File type      | File extension | Description                                  |
+| -------------- | -------------- | -------------------------------------------- |
+| Text           | `.txt`         | An unformatted text document.                |
+| Adobe PDF      | `.pdf`         | A portable document file formatted document. |
+| Microsoft Word | `.docx`        | A Microsoft Word document file.              |
+
+The following input constraints apply:
+
+| Attribute                      | Limit    |
+| ------------------------------ | -------- |
+| Total documents per request    | <= 20    |
+| Total content size per request | <= 10 MB |
+
+The following content types are not supported:
+
+| Type                        | Limitation                                           |
+| --------------------------- | ---------------------------------------------------- |
+| Fully scanned PDFs          | Not supported.                                       |
+| Images with embedded text   | Digital images with embedded text are not supported. |
+| Tables in scanned documents | Not supported.                                       |
+
+See [language support](language-support.md) and [quotas and limits](../concepts/data-limits.md) for current language coverage and service limit details.
+
+## Pricing
+
+Document-based PII redaction uses Azure AI Language pricing. For current pricing details, see [Azure AI Language pricing](https://aka.ms/unifiedLanguagePricing).
+
+## Next steps
+
+Use the following references to continue implementation:
+
+* [Create SAS tokens for storage containers](../native-document-support/shared-access-signatures.md)
+* [Create a managed identity for storage containers](../native-document-support/managed-identities.md)

@@ -1,7 +1,7 @@
 ---
-title: Adapt Personally Identifying Information (PII) to your domain
+title: Adapt Personally Identifiable Information (PII) to your domain
 titleSuffix: Foundry Tools
-description: This article shows you how to adapt Personally Identifying Information (PII) to your domain.
+description: This article shows you how to adapt Personally Identifiable Information (PII) to your domain.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
@@ -10,7 +10,7 @@ ms.date: 11/18/2025
 ms.author: lajanuar
 ms.custom: language-service-pii
 ---
-# Adapt Personally Identifying Information (PII) to your domain
+# Adapt Personally Identifiable Information (PII) to your domain
 
 To support using your own terminology to identify entities (also known as *context*), the `entitySynonyms` feature enables you to define custom synonyms for specific entity types. This feature helps the system detect entities that appear in your inputs using terms or vocabulary that the model doesn't recognize by default. Aligning your specific terms with standard entities allows the model to accurately recognize and link these terms during entity detection.
 

@@ -1,7 +1,7 @@
 ---
-title: Identify and extract Personally Identifying Information (PII) from conversations
+title: Identify and extract Personally Identifiable Information (PII) from conversations
 titleSuffix: Foundry Tools
-description: This article shows you how to detect and redact Personally Identifying Information (PII) from speech, chat, and spoken-word transcriptions and call transcripts.
+description: This article shows you how to detect and redact Personally Identifiable Information (PII) from speech, chat, and spoken-word transcriptions and call transcripts.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
@@ -10,7 +10,7 @@ ms.date: 04/03/2026
 ms.author: lajanuar
 ms.custom: language-service-pii
 ---
-# Detect and redact Personally Identifying Information in conversations
+# Detect and redact Personally Identifiable Information in conversations
 
 Azure Language in Foundry Tools conversation PII API analyzes audio discourse to identify and redact sensitive information (PII) using various predefined categories. This API works on both transcribed text (referred to as transcripts) and chats. For transcripts, it also facilitates the redaction of audio segments containing PII by providing the timing information for those segments.
 

@@ -1,7 +1,7 @@
 ---
-title: Identify and extract Personally Identifying Information (PII) from native documents
+title: Identify and extract Personally Identifiable Information (PII) from native documents
 titleSuffix: Foundry Tools
-description: This article shows you how to redact Personally Identifying Information (PII) from native documents.
+description: This article shows you how to redact Personally Identifiable Information (PII) from native documents.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
@@ -10,7 +10,7 @@ ms.date: 11/18/2025
 ms.author: lajanuar
 ms.custom: language-service-pii
 ---
-# Detect and redact Personally Identifying Information in native documents (preview)
+# Detect and redact Personally Identifiable Information in native documents (preview)
 
 > [!IMPORTANT]
 >

@@ -1,7 +1,7 @@
 ---
-title: Identify and extract Personally Identifying Information (PII) from text
+title: Identify and extract Personally Identifiable Information (PII) from text
 titleSuffix: Foundry Tools
-description: This article shows you how to identify, extract, and redact Personally Identifying Information (PII) from text.
+description: This article shows you how to identify, extract, and redact Personally Identifiable Information (PII) from text.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
@@ -11,7 +11,7 @@ ms.author: lajanuar
 ms.custom: language-service-pii
 ---
 <!-- markdownlint-disable MD025 -->
-# Detect and redact Personally Identifying Information in text
+# Detect and redact Personally Identifiable Information in text
 
 Azure Language in Foundry Tools is a cloud-based service that applies Natural Language Processing (NLP) features to text-based data. The PII feature can evaluate unstructured text, extract, and redact sensitive information (PII) and health information (PHI) in text across several [predefined categories](../concepts/entity-categories.md).
 
@@ -318,4 +318,4 @@ When you get results from PII detection, you can stream the results to an applic
 
 ## Next steps
 
-[Personally Identifying Information (PII) overview](../overview.md)
+[Personally Identifiable Information (PII) overview](../overview.md)

@@ -1,7 +1,7 @@
 ---
 title: Use personally identifiable information (PII) detection Docker containers on-premises
 titleSuffix: Foundry Tools
-description: Use Docker containers for the Personally Identifiable Information (PII) detection API to determine the language of written text, on-premises.
+description: Use Docker containers for the Personally Identifiable Information (PII) detection API to detect and redact sensitive entities on-premises.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language

@@ -12,5 +12,5 @@ To use PII detection, you submit text for analysis and handle the API output in
 |Development option  |Description  |
 |---------|---------|
 |[**Microsoft Foundry (new)**](https://ai.azure.com/) portal| Foundry (new) is a cloud-based AI platform that provides streamlined access to Foundry models, agents, and tools through Foundry projects.
-|[**Foundry (classic)**](https://ai.azure.com/) portal| Foundry (classic) is a cloud-based platform that supports hub-based projects and other resource types. When you sign up, you can use your own data to detect personally identifying information within text examples.|
+|[**Foundry (classic)**](https://ai.azure.com/) portal| Foundry (classic) is a cloud-based platform that supports hub-based projects and other resource types. When you sign up, you can use your own data to detect personally identifiable information within text examples.|
 |[**REST API or Client library (Azure SDK)**](/rest/api/language/analyze-text/analyze-text/analyze-text)| Integrate PII detection into your applications using the REST API, or the client library available in various languages.|

@@ -60,7 +60,7 @@ Select either **Extract PII from conversation** or **Extract PII from text** fro
 
 ### Extract PII from conversation
 
-**Extract PII from conversation** is designed to identify and mask personally identifying information in conversational text.
+**Extract PII from conversation** is designed to identify and mask personally identifiable information in conversational text.
 
 In **Configuration** there are the following options:
 
@@ -91,7 +91,7 @@ Verify that each PII entity appears highlighted with the correct category label.
 
 ### Extract PII from text
 
-**Extract PII from text** is designed to identify and mask personally identifying information in text.
+**Extract PII from text** is designed to identify and mask personally identifiable information in text.
 
 In **Configuration** you can select from the following options:
 
@@ -161,7 +161,7 @@ There are two ways to access the PII interface:
 
 ## Detect PII in the Foundry playground
 
-The **extract PII from text** feature detects and masks personally identifying information within written content.
+The **extract PII from text** feature detects and masks personally identifiable information within written content.
 
 1. On the **Playground** tab, select the sample text, use the paperclip icon to upload your text, or enter your own text.
 

@@ -52,7 +52,7 @@ In a code editor, create a new file named `test_pii_payload.json` and copy the f
 
 Save `test_pii_payload.json` somewhere on your computer. For example, your desktop.  
 
-## Send a personally identifying information (PII) detection API request
+## Send a personally identifiable information (PII) detection API request
 
 Use the following commands to send the API request using the program you're using. Copy the command into your terminal, and run it.
 

@@ -1,7 +1,7 @@
 ---
 title: Personally Identifiable Information (PII) detection language support
 titleSuffix: Foundry Tools
-description: This article explains which natural languages the PII detection feature supports of Azure Language in Foundry Tools.
+description: This article explains which natural languages the PII detection feature in Azure Language in Foundry Tools supports.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
@@ -10,11 +10,16 @@ ms.date: 11/18/2025
 ms.author: lajanuar
 ms.custom: language-service-pii, build-2024
 ---
+<!-- markdownlint-disable MD025 -->
 # Personally Identifiable Information (PII) detection language support
 
-Use this article to learn which natural languages text PII, document PII, and conversation PII features support.
+Use this article to learn which natural languages each PII feature type supports:
 
-# [Text PII](#tab/text)
+* Text PII
+* Conversation PII
+* Document-based PII
+
+# [Text PII](#tab/text-pii)
 
 ## Text PII language support
 
@@ -100,9 +105,26 @@ Use this article to learn which natural languages text PII, document PII, and co
 |Vietnamese           |`vi`         |                  |
 |Welsh                |`cy`         |                  |
 
-# [PII for documents](#tab/documents)
+# [Conversation PII](#tab/conversation-pii)
+
+## Conversation PII language support
+
+Conversation PII preview version `2023-04-15-preview` supports the following languages:
+
+* English
+* French
+* German
+* Spanish
+
+Conversation PII generally available (GA) version currently supports the following languages:
+
+* English
+* French
+* Spanish
+
+# [Document-based PII](#tab/document-based-pii)
 
-## PII language support
+## Document-based PII language support
 
 |Language             |Language code|Notes             |
 |---------------------|-------------|------------------|
@@ -186,28 +208,13 @@ Use this article to learn which natural languages text PII, document PII, and co
 |Chinese-Simplified   |`zh-hans`    |`zh` also accepted|
 |Chinese-Traditional  |`zh-hant`    |                  |
 
-# [PII for conversations](#tab/conversations)
-
-## PII language support
-
-PII conversation preview version `2023-04-15-preview` supports the following languages:
-
-* English
-* French
-* German
-* Spanish
-
-
-PII conversation generally available (GA) version currently supports the following languages:
-
-* English
-* French
-* Spanish
-
-
-
 ---
 
 ## Next steps
 
-[PII feature overview](overview.md)
+Use these PII pages for implementation details:
+
+* [Text PII overview](text-pii-overview.md)
+* [Conversation PII overview](conversation-pii-overview.md)
+* [Document-based PII overview](document-based-pii-overview.md)
+* [PII feature overview](overview.md)

@@ -1,23 +1,23 @@
 ---
-title: What is the Personally Identifying Information (PII) detection feature in Azure Language?
+title: What is the Personally Identifiable Information (PII) detection feature in Azure Language?
 titleSuffix: Foundry Tools
 description: An overview of the PII detection feature in Azure Language, which helps you extract entities and sensitive information (PII) in text.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 03/30/2026
+ms.date: 04/16/2026
 ms.author: lajanuar
 ms.custom: language-service-pii
 ---
 
 <!-- markdownlint-disable MD025 -->
 # What is PII detection in Azure Language?
 
-Personally Identifiable Information (PII) detection is an Azure Language [core capability](../overview.md#core-capabilities). The PII detection service is a cloud-based API that uses machine learning to **identify and redact** sensitive information from input data. The service classifies sensitive personal data into predefined categories such as phone numbers, email addresses, and identification documents.
+Personally Identifiable Information (PII) detection is an Azure Language [core capability](../overview.md#core-capabilities) that helps you identify, classify, and redact sensitive data across text, conversations, and native documents. Submit input text to the service and receive structured output with entity categories, confidence scores, and redacted results based on your API configuration. You can use this capability to implement privacy controls, reduce sensitive data exposure, and support compliance requirements in application and data-processing workflows.
 
 > [!TIP]
-> Try PII detection [in Microsoft Foundry portal](https://ai.azure.com/). There you can [utilize a currently existing Language Studio resource or create a new Foundry resource](../../../ai-services/connect-services-foundry-portal.md).
+> Try PII detection in [Microsoft Foundry](https://ai.azure.com/) and choose the feature type that matches your input data.
 
 ## Video demonstration
 
@@ -31,105 +31,120 @@ In this video, we introduce the PII detection service and show you how it detect
 
 Closed captions are available for this video.
 
-## Capabilities
+## PII documentation by feature type
 
- Currently, PII support is available for the following capabilities:
+PII capabilities are grouped by feature type. Each feature type maps to a specific input format and processing model.
 
-* [General text PII detection](how-to/redact-text-pii.md) for processing sensitive information (PII) and health information (PHI) in unstructured text across several [predefined categories](concepts/entity-categories.md).
-* [Conversation PII detection](how-to/redact-conversation-pii.md), a specialized model designed to handle speech transcriptions and the informal, conversational tone found in meeting and call transcripts.
-* [Native Document PII detection](how-to/redact-document-pii.md) for processing structured document files.
+:::image type="content" source="media/feature-types.png" alt-text="Screenshot of PII feature types diagram.":::
 
-### [Text PII](#tab/text-pii)
+Choose the feature type that matches your data shape and runtime requirements.
 
-Language is a cloud-based service that applies Natural Language Processing (NLP) features to detect categories of personal information (PII) in text-based data. This documentation contains the following types:
+### Text PII
 
-* **[Quickstarts](quickstart.md)** are getting-started instructions to guide you through making requests to the service.
-* **[How-to guides](how-to/redact-text-pii.md)** contain instructions for using the service in more specific or customized ways.
+[**Text PII**](text-pii-overview.md) processes string-based payloads and returns synchronous detection and redaction results. Use this feature when your system handles request-time processing for messages, prompts, logs, and other text fields.
 
-[!INCLUDE [Typical workflow for pre-configured language features](../includes/overview-typical-workflow.md)]
+Use the following documentation to implement and tune Text PII workloads:
 
-### Key features for text PII
+* [Quickstart: Detect personally identifiable information (PII)](quickstart.md)
+* [Detect and redact Personally Identifiable Information in text](how-to/redact-text-pii.md)
+* [Text PII recognized entity categories (extended format)](concepts/entity-categories.md)
+* [Text PII recognized entity categories (list format)](concepts/entity-categories-list.md)
 
-Language offers named entity recognition to identify and categorize information within your text. The feature detects PII categories including names, organizations, addresses, phone numbers, financial account numbers or codes, and government identification numbers. A subset of this PII is protected health information (PHI). By specifying domain=phi in your request, only PHI entities are returned.
+### Conversation PII
 
-### [Conversation PII](#tab/conversation-pii)
+[**Conversation PII**](conversation-pii-overview.md) processes multi-turn exchanges and transcript-oriented payloads where turn boundaries and conversation context affect detection and masking behavior. Use this feature for asynchronous workloads that analyze chat and transcript structures.
 
-The Language conversation PII API processes audio conversations to detect and remove sensitive information (PII) based on a set of predefined categories. This documentation contains the following types:
+Use the following documentation to implement Conversation PII job-based processing:
 
-* **[Quickstarts](quickstart.md)** are getting-started instructions to guide you through making requests to the service.
-* **[How-to guides](how-to/redact-conversation-pii.md)** contain instructions for using the service in more specific or customized ways.
+* [Conversation PII overview](conversation-pii-overview.md)
+* [Detect and redact Personally Identifiable Information in conversations](how-to/redact-conversation-pii.md)
+* [Conversation PII recognized entity categories (extended format)](concepts/conversations-entity-categories.md)
+* [Conversation PII recognized entity categories (list format)](concepts/conversations-entities-list.md)
 
-### Key features for conversation PII
+### Document-based PII
 
-Conversation PII uses natural language processing techniques to identify and categorize information within conversations. This feature supports both natural chat transcripts and transcribed transcripts from phone calls. For a chat or call, there are different kinds of important information, scattered over long text or transcripts.
+[**Document-based PII**](document-based-pii-overview.md) processes native files and returns redaction output that preserves document structure while also producing machine-readable metadata. Use this feature for asynchronous, storage-based pipelines that handle `.pdf`, `.docx`, and `.txt` inputs.
 
-### [Native document PII](#tab/native-document-pii)
+Use the following documentation to implement Document-based PII in native-file pipelines:
 
-The native document support feature allows you to send API requests asynchronously. You can use an HTTP POST request body to transmit your data and an HTTP GET request query string to check the status of your requests. Your processed documents are stored in your designated Azure Blob Storage container. This documentation contains the following types:
+* [Document-based PII overview](document-based-pii-overview.md)
+* [Detect and redact Personally Identifiable Information in native documents](how-to/redact-document-pii.md)
 
-* **[Quickstarts](quickstart.md)** are getting-started instructions to guide you through making requests to the service.
-* **[How-to guides](how-to/redact-document-pii.md)** contain instructions for using the service in more specific or customized ways.
+---
 
-### Key features for native document PII
+## Choose the right PII feature
 
-Document PII uses natural language processing techniques to identify and categorize information within documents.
+Use the following table to select the right experience before you start implementation:
 
----
+| Feature type | Input | Best for | Key strength |
+| --- | --- | --- | --- |
+| [Text PII](text-pii-overview.md) | Raw text strings | Apps, prompts, logs, tickets | Broad language coverage and flexible redaction options |
+| [Conversation PII](conversation-pii-overview.md) | Turn-based chat or transcript data | Contact centers, meetings, voice transcripts | Conversational context and transcript-aware output |
+| [Document-based PII](document-based-pii-overview.md) | Native files (`.pdf`, `.docx`, `.txt`) | Compliance workflows and document sharing | Redacted files with document fidelity and JSON metadata |
 
-## Get started with PII detection
+## Get started
 
 [!INCLUDE [development options](./includes/development-options.md)]
 
-[!INCLUDE [Developer reference](../includes/reference-samples-text-analytics.md)]
+[!INCLUDE [Typical workflow for pre-configured language features](../includes/overview-typical-workflow.md)]
 
-## Input requirements and service limits
+## What differs across feature types?
 
-### [Text PII](#tab/text-pii)
+All feature types use predefined entity categories and return confidence-scored detections. They differ mainly by input format and processing model:
 
-* Text PII takes text for analysis. For more information, see [Data and service limits](../concepts/data-limits.md) in the how-to guide.
-* PII works with various written languages. For more information, see [language support](language-support.md?tabs=text-summarization). You can specify in which [supported languages](../concepts/language-support.md) your source text is written. If you don't specify a language, the extraction defaults to English. The API may return offsets in the response to support different [multilingual and emoji encodings](../concepts/multilingual-emoji-support.md).
+* **Text PII** is optimized for synchronous string-based input.
+* **Conversation PII** is optimized for turn-based transcript and chat structures.
+* **Document-based PII** is asynchronous and optimized for processing native files while preserving document structure.
 
-### [Conversation PII](#tab/conversation-pii)
+> [!NOTE]
+> **Document-based PII** focuses on native-file redaction workflows. Some text-only options are not available in every document API version.
 
-* Conversation PII takes structured text for analysis. For more information, see [data and service limits](../concepts/data-limits.md).
-* Conversation summarization works with various spoken languages. For more information, see [language support](language-support.md?tabs=conversation-summarization).
-* [!INCLUDE [service limits article](../includes/service-limits-link.md)]
+## GA and preview guidance
 
-### [Native document PII](#tab/native-document-pii)
+To avoid integration issues, use API versions and features that match your deployment target:
 
-* Native document PII takes text for analysis. For more information, see [Data and service limits](../concepts/data-limits.md) in the how-to guide.
-* Native document PII works with various written languages. For more information, see [language support](language-support.md?tabs=document-summarization).
+* Use generally available (GA) API versions for production workloads.
+* Use preview API versions only when you need preview-only features.
+* Avoid combining request payload examples from different API versions.
 
-A native document refers to the file format used to create the original document such as Microsoft Word (docx) or a portable document file (pdf). Native document support eliminates the need for text preprocessing before using Language resource capabilities. Currently, native document support is available for the [**PiiEntityRecognition**](../personally-identifiable-information/concepts/entity-categories.md) capability.
+Each feature-specific how-to article identifies preview-only sections where applicable.
 
- Currently **PII** supports the following native document formats:
+## Input requirements and service limits
 
-|File type|File extension|Description|
-|---------|--------------|-----------|
-|Text| `.txt`|An unformatted text document.|
-|Adobe PDF| `.pdf`       |A portable document file formatted document.|
-|Microsoft Word|`.docx`|A Microsoft Word document file.|
+Use the following references to verify language coverage, service limits, and model-version behavior:
 
----
+* [Language support for text, document, and conversation PII](language-support.md)
+* [Quotas and limits](../concepts/data-limits.md)
+* [Model lifecycle and API version guidance](../concepts/model-lifecycle.md)
+
+[!INCLUDE [Developer reference](../includes/reference-samples-text-analytics.md)]
 
 ## Responsible AI
 
-An AI system includes not only the technology, but also the people who use it, the people affected by it, and the deployment environment. Read the [transparency note for PII](/azure/ai-foundry/responsible-ai/language-service/transparency-note-personally-identifiable-information) to learn about responsible AI use and deployment in your systems. For more information, see the following articles:
+You should design responsible solutions by considering the model behavior, the users who operate the system, and the people affected by the output. Read the [transparency note for PII](/azure/ai-foundry/responsible-ai/language-service/transparency-note-personally-identifiable-information) to understand responsible deployment guidance. For more information, see the following articles:
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 
-## Example scenarios
+## Common use cases
+
+PII detection is useful when you need to apply privacy controls before storage, analytics, sharing, or downstream AI processing.
+
+Typical examples include:
 
-* **Apply sensitivity labels** - For example, based on the results from the PII service, a public sensitivity label might be applied to documents where no PII entities are detected. For documents where US addresses and phone numbers are recognized, a confidential label might be applied. A highly confidential label might be used for documents where bank routing numbers are recognized.
-* **Redact some categories of personal information from documents that get wider circulation** - For example, if customer contact records are accessible to frontline support representatives, the company can redact the customer's personal information besides their name from the version of the customer history to preserve the customer's privacy.
-* **Redact personal information in order to reduce unconscious bias** - For example, during a company's resume review process, they can block name, address, and phone number to help reduce unconscious gender or other biases.
-* **Replace personal information in source data for machine learning to reduce unfairness** – For example, if you want to remove names that might reveal gender when training a machine learning model, you could use the service to identify them and you could replace them with generic placeholders for model training.
-* **Remove personal information from call center transcription** – For example, if you want to remove names or other PII data that happen between the agent and the customer in a call center scenario. You could use the service to identify and remove them.
-* **Data cleaning for data science** - PII can be used to make the data ready for data scientists and engineers to be able to use these data to train their machine learning models. Redacting the data to make sure that customer data isn't exposed.
+* Applying sensitivity labels based on detected PII categories.
+* Redacting personal information in documents that are distributed more broadly.
+* Masking personal identifiers in resume screening workflows to reduce bias risk.
+* Replacing sensitive values with placeholders in machine learning training datasets.
+* Redacting names and contact details in call center transcription workflows.
+* Preparing datasets for analytics and data science without exposing customer data.
 
 ## Next steps
 
-There are two ways to get started using the entity linking feature:
+Use the following references to continue implementation:
 
-* [Foundry](../../../ai-foundry/what-is-foundry.md) is a web-based platform that lets you use several Language features without needing to write code.
-* The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.
+* [Quickstart: Detect personally identifiable information (PII)](quickstart.md)
+* [Detect and redact Personally Identifiable Information in text](how-to/redact-text-pii.md)
+* [Detect and redact Personally Identifiable Information in conversations](how-to/redact-conversation-pii.md)
+* [Detect and redact Personally Identifiable Information in native documents](how-to/redact-document-pii.md)
+* [Language support for text, document, and conversation PII](language-support.md)
+* [Quotas and limits](../concepts/data-limits.md)

@@ -22,15 +22,15 @@ zone_pivot_groups: programming-languages-text-analytics
 ai-usage: ai-assisted
 ---
 
-# Quickstart: Detect personally identifiable information (PII)
 <!-- markdownlint-disable MD025 -->
+# Quickstart: Detect personally identifiable information (PII)
 
 In this quickstart, you use the Azure Language in Foundry Tools PII detection feature to identify and redact personally identifiable information in text. You can get started using your preferred client library, the REST API, or the Microsoft Foundry portal.
 
 If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn) before you begin.
 
 > [!NOTE]
-> This quickstart covers PII detection in documents. To detect PII in conversations, see [How to detect and redact PII in conversations](how-to/redact-conversation-pii.md). To detect PII in text, see [How to detect and redact PII in text](how-to/redact-text-pii.md).
+> This quickstart focuses on text PII. For conversation workflows, see [How to detect and redact PII in conversations](how-to/redact-conversation-pii.md). For document workflows, see [Document-based PII overview](document-based-pii-overview.md) and [How to detect and redact PII in native documents](how-to/redact-document-pii.md).
 
 ::: zone pivot="programming-language-csharp"
 
@@ -88,6 +88,7 @@ If you no longer need the resources you created in this quickstart, delete the i
 ## Related content
 
 * [PII overview](overview.md)
+* [Document-based PII overview](document-based-pii-overview.md)
 * [How to detect and redact PII in text](how-to/redact-text-pii.md)
 * [How to detect and redact PII in conversations](how-to/redact-conversation-pii.md)
 * [Supported entity categories for text](concepts/entity-categories.md)

@@ -0,0 +1,90 @@
+---
+title: Text Personally Identifiable Information (PII) redaction overview
+titleSuffix: Foundry Tools
+description: Learn how text PII redaction in Azure Language detects and redacts sensitive data in raw text for applications and workflows.
+author: laujan
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: overview
+ms.date: 04/16/2026
+ms.author: lajanuar
+ms.custom: language-service-pii
+---
+
+<!-- markdownlint-disable MD025 -->
+# Text Personally Identifiable Information (PII) redaction overview
+
+Text PII redaction in Azure AI Language helps you detect and redact sensitive data in raw text strings. You can use this feature when your application handles logs, prompts, forms, chat messages, or other text content directly.
+
+Text PII is optimized for synchronous request/response integration and configurable redaction behavior, so you can apply PII controls inline in application and data-processing workflows.
+
+## At a glance
+
+Text PII provides the following capabilities:
+
+* Direct text redaction for unstructured string input.
+* Low-latency request/response integration for application pipelines.
+* Configurable entity filters and redaction policies.
+* Structured entity output with categories, offsets, and confidence scores.
+
+## Why use text PII?
+
+Text PII is optimized for low-latency integration and direct request/response workflows. It supports broad language coverage and configurable redaction behavior for many real-time and batch scenarios.
+
+Text PII is especially useful when you need to:
+
+* Redact sensitive entities in user-generated text.
+* Integrate PII protection directly into application pipelines.
+* Apply entity filters and redaction policies in API requests.
+
+## What it returns
+
+When a request succeeds, the service returns:
+
+* Detected entities with categories, subcategories, offsets, and confidence scores.
+* Redacted text output based on your selected redaction behavior.
+
+## How it works
+
+Text PII is typically used with a synchronous workflow:
+
+1. Submit text input for analysis.
+2. Configure optional parameters like entity filters and redaction behavior.
+3. Process entities and redacted output in your application.
+
+For implementation details and request samples, see [Detect and redact Personally Identifiable Information in text](how-to/redact-text-pii.md).
+
+## How it differs from other PII feature types
+
+All PII feature types use predefined entity categories, but they optimize for different input types:
+
+* Text PII is optimized for direct string-based input and app integration.
+* Conversation PII is optimized for turn-based conversational structures.
+* Document-based PII is optimized for native-file workflows and file output fidelity.
+
+## Common use cases
+
+Text PII is useful when you need to detect and redact sensitive data before storage, analytics, sharing, or downstream AI processing.
+
+Typical examples include:
+
+* User input and output fields in web and mobile applications.
+* Application logs and telemetry data streams.
+* Prompt and response filtering in AI workflows.
+* Batch preprocessing for unstructured text datasets.
+
+## Supported formats and limits
+
+* Input format: raw text content
+* See [language support](language-support.md) and [quotas and limits](../concepts/data-limits.md) for current support details.
+
+## Pricing
+
+Text PII redaction uses Azure AI Language pricing. For current pricing details, see [Azure AI Language pricing](https://aka.ms/unifiedLanguagePricing).
+
+## Next steps
+
+Use the following references to continue implementation:
+
+* [Detect and redact Personally Identifiable Information in text](how-to/redact-text-pii.md)
+* [PII feature overview](overview.md)

@@ -5,7 +5,7 @@ ms.service: azure-ai-language
 author: laujan
 ms.author: lajanuar
 ms.topic: limits-and-quotas
-ms.date: 12/11/2025
+ms.date: 04/20/2026
 ---
 
 # Project limits and boundaries
@@ -42,7 +42,6 @@ File names may not include the following characters:
 |Format|Max file size (MB)|
 |--|--|
 |`.docx`|10|
-|`.pdf`|25|
 |`.tsv`|10|
 |`.txt`|10|
 |`.xlsx`|3|
@@ -84,15 +83,17 @@ The length and acceptable characters for metadata name and value are listed in t
 |||||
 
 ## Project content limits
+
 Overall limits on the content in the project:
+
 * Length of answer text: 25,000 characters
 * Length of question text: 1,000 characters
 * Length of metadata key text: 100 characters
 * Length of metadata value text: 500 characters
 * Supported characters for metadata name: Alphabets, digits, and `_`
 * Supported characters for metadata value: All except `:` and `|`
 * Length of file name: 200
-* Supported file formats: ".tsv", ".pdf", ".txt", ".docx", ".xlsx".
+* Supported file formats: ".tsv", ".txt", ".docx", ".xlsx".
 * Maximum number of alternate questions: 300
 * Maximum number of question-answer pairs: Depends on the **[Azure AI Search tier](/azure/search/search-limits-quotas-capacity#document-limits)** chosen. A question and answer pair maps to a document on Azure AI Search index.
 * URL/HTML page: 1 million characters

@@ -285,7 +285,7 @@ curl -X GET -H "Ocp-Apim-Subscription-Key: {API-KEY}" -H "Content-Type: applicat
           },
           {
             "displayName": "source2",
-            "sourceUri": "https://download.microsoft.com/download/2/9/B/29B20383-302C-4517-A006-B0186F04BE28/surface-pro-4-user-guide-EN.pdf",
+            "sourceUri": "https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features",
             "sourceKind": "file",
             "contentStructureKind": "unstructured",
             "lastUpdatedDateTime": "2021-05-01T15:13:22Z"
@@ -601,7 +601,7 @@ curl -X PATCH -H "Ocp-Apim-Subscription-Key: {API-KEY}" -H "Content-Type: applic
     "value": {
       "displayName": "source5",
       "sourceKind": "url",
-      "sourceUri": "https://download.microsoft.com/download/7/B/1/7B10C82E-F520-4080-8516-5CF0D803EEE0/surface-book-user-guide-EN.pdf",
+      "sourceUri": "https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features",
       "sourceContentStructureKind": "semistructured"
     }
   }

@@ -5,7 +5,7 @@ ms.service: azure-ai-language
 ms.topic: how-to
 author: laujan
 ms.author: lajanuar
-ms.date: 12/15/2025
+ms.date: 04/20/2026
 ms.custom: language-service-question-answering, mode-other
 ---
 # Create, test, and deploy: CQA knowledge base
@@ -81,12 +81,12 @@ A CQA knowledge base is a structured set of question-and-answer pairs optimized
 1. In the **Add URLs** window, add the following values:
 
    * **URL name**: **Surface Book User Guide**
-   * **URL**: **https://download.microsoft.com/download/7/B/1/7B10C82E-F520-4080-8516-5CF0D803EEE0/surface-book-user-guide-EN.pdf** 
+   * **URL**: **<https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features>**
    * **Classify file structure**: Leave the default setting (**Auto-detect**)
 
      :::image type="content" source="../media/agents/add-urls.png" alt-text="Screenshot of the select url source selection and add button in the Foundry.":::
 
-1. Finally, select the **Add URLs** button. 
+1. Finally, select the **Add URLs** button.
 
     The extraction process requires a short amount of time to analyze the document and detect questions and answers. During this step, the service evaluates whether the content is structured or unstructured.<br>
 

@@ -6,7 +6,7 @@ ms.topic: how-to
 author: laujan
 ms.author: lajanuar
 recommendations: false
-ms.date: 12/15/2025
+ms.date: 04/20/2026
 ---
 # Export-import-refresh in custom question answering
 
@@ -21,7 +21,6 @@ You might want to create a copy of your custom question answering project or rel
 * An Azure subscription. You can [create one for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn) before you begin.
 * A [language resource](https://aka.ms/create-language-resource) with the custom question answering feature enabled. Remember your Microsoft Entra ID, Subscription, language resource name you selected when you created the resource.
 
-
 ### Export a project programmatically
 
 To automate the export process, use the [export functionality of the authoring API](./authoring.md#export-project-metadata-and-assets)
@@ -49,14 +48,13 @@ curl -X PATCH -H "Ocp-Apim-Subscription-Key: {API-KEY}" -H "Content-Type: applic
     "value": {
       "displayName": "source5",
       "sourceKind": "url",
-      "sourceUri": https://download.microsoft.com/download/7/B/1/7B10C82E-F520-4080-8516-5CF0D803EEE0/surface-book-user-guide-EN.pdf,
+      "sourceUri": "https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features",
       "refresh": "true"
     }
   }
 ]'  -i 'https://{ENDPOINT}.cognitiveservices.azure.com/language/query-knowledgebases/projects/{PROJECT-NAME}/sources?api-version=2021-10-01'
 ```
 
-
 ## Next steps
 
 * [Learn how to use the Authoring API](./authoring.md)

@@ -1,7 +1,7 @@
 ---
 title: "Quickstart: Use cURL & REST to manage project - custom question answering"
 description: This quickstart shows you how to create, publish, and query your project using the REST APIs.
-ms.date: 12/15/2025
+ms.date: 04/20/2026
 ms.topic: include
 author: laujan
 ms.author: lajanuar
@@ -11,9 +11,9 @@ ms.author: lajanuar
 * The current version of [cURL](https://curl.haxx.se/). Several command-line switches are used in the quickstarts, which are noted in the [cURL documentation](https://curl.haxx.se/docs/manpage.html).
 * Azure subscription - [Create one for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn)
 * Custom question answering requires a [Language resource](https://portal.azure.com/?quickstart=true#create/Microsoft.CognitiveServicesTextAnalytics) with the custom question answering feature enabled to generate an API key and endpoint.
-    * After your Language resource deploys, select **Go to resource**. You need the key and endpoint from the resource you create to connect to the API. Paste your key and endpoint into the code later in the quickstart.
+* After your Language resource deploys, select **Go to resource**. You need the key and endpoint from the resource you create to connect to the API. Paste your key and endpoint into the code later in the quickstart.
 * Create a Language resource with [Azure CLI](../../../multi-service-resource.md?pivots=azcli) and provide the following properties: `--api-properties qnaAzureSearchEndpointId=/subscriptions/<azure-subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.Search/searchServices/<azure-search-service-name> qnaAzureSearchEndpointKey=<azure-search-service-auth-key>`
-* An existing project to query. If you have not setup a project, you can follow the instructions in the [**Foundry quickstart**](../quickstart/sdk.md). Or add a project that uses this [Surface User Guide URL](https://download.microsoft.com/download/7/B/1/7B10C82E-F520-4080-8516-5CF0D803EEE0/surface-book-user-guide-EN.pdf) as a data source.
+* An existing project to query. If you have not setup a project, you can follow the instructions in the [**Foundry quickstart**](../quickstart/sdk.md). Or add a project that uses this [Surface User Guide URL](https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features) as a data source.
 
 
 

@@ -4,7 +4,7 @@ description: This quickstart shows how to get started with the custom question a
 author: laujan
 ms.author: lajanuar
 ms.topic: include
-ms.date: 12/15/2025
+ms.date: 04/20/2026
 ---
 Use this quickstart for the custom question answering client library for .NET to:
 
@@ -24,9 +24,9 @@ Use this quickstart for the custom question answering client library for .NET to
 * Azure subscription - [Create one for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn)
 * The [Visual Studio IDE](https://visualstudio.microsoft.com/vs/) or current version of [.NET Core](https://dotnet.microsoft.com/download/dotnet-core).
 * Custom question answering requires a [Language resource](https://portal.azure.com/?quickstart=true#create/Microsoft.CognitiveServicesTextAnalytics) with the custom question answering feature enabled to generate an API key and endpoint. 
-    * After your Language resource deploys, select **Go to resource**. You need the key and endpoint from the resource you create to connect to the API. Paste your key and endpoint into the code later in the quickstart.
+* After your Language resource deploys, select **Go to resource**. You need the key and endpoint from the resource you create to connect to the API. Paste your key and endpoint into the code later in the quickstart.
 * Create a Language resource with [Azure CLI](../../../multi-service-resource.md?pivots=azcli) and provide the following properties: `--api-properties qnaAzureSearchEndpointId=/subscriptions/<azure-subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.Search/searchServices/<azure-search-service-name> qnaAzureSearchEndpointKey=<azure-search-service-auth-key>`
-* An existing project to query. If you don't have a project, you can follow the instructions in the [**Microsoft Foundry quickstart**](../quickstart/sdk.md). Or add a project that uses this [Surface User Guide URL](https://download.microsoft.com/download/7/B/1/7B10C82E-F520-4080-8516-5CF0D803EEE0/surface-book-user-guide-EN.pdf) as a data source.
+* An existing project to query. If you don't have a project, you can follow the instructions in the [**Microsoft Foundry quickstart**](../quickstart/sdk.md). Or add a project that uses this [Surface User Guide URL](https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features) as a data source.
 
 
 

@@ -4,7 +4,7 @@ description: This quickstart shows how to get started with the custom question a
 ms.topic: include
 author: laujan
 ms.author: lajanuar
-ms.date: 12/15/2025
+ms.date: 04/20/2026
 ---
 Use this quickstart for the custom question answering client library for Python to:
 
@@ -25,7 +25,7 @@ Use this quickstart for the custom question answering client library for Python
 * Custom question answering requires a [Language resource](https://portal.azure.com/?quickstart=true#create/Microsoft.CognitiveServicesTextAnalytics) with the custom question answering feature enabled to generate an API key and endpoint.
     * After your Language resource deploys, select **Go to resource**. You need the key and endpoint from the resource you create to connect to the API. Paste your key and endpoint into the code later in the quickstart.
 * Create a Language resource with [Azure CLI](../../../multi-service-resource.md?pivots=azcli) and provide the following properties: `--api-properties qnaAzureSearchEndpointId=/subscriptions/<azure-subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.Search/searchServices/<azure-search-service-name> qnaAzureSearchEndpointKey=<azure-search-service-auth-key>`
-* An existing project to query. If you don't have a project, you can follow the instructions in the [**Microsoft Foundry quickstart**](../quickstart/sdk.md). Or add a project that uses this [Surface User Guide URL](https://download.microsoft.com/download/7/B/1/7B10C82E-F520-4080-8516-5CF0D803EEE0/surface-book-user-guide-EN.pdf) as a data source.
+* An existing project to query. If you don't have a project, you can follow the instructions in the [**Microsoft Foundry quickstart**](../quickstart/sdk.md). Or add a project that uses this [Surface User Guide URL](https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features) as a data source.
 
 
 

@@ -5,8 +5,9 @@ ms.service: azure-ai-language
 ms.author: lajanuar
 author: laujan
 ms.topic: reference
-ms.date: 12/15/2025
+ms.date: 04/20/2026
 ---
+
 # Format guidelines for custom question answering
 
 Review these formatting guidelines to get the best results for your content.
@@ -31,7 +32,7 @@ Custom question answering identifies sections and subsections and relationships
 
 ### Product manuals
 
-A manual is typically guidance material that accompanies a product. It helps the user to set up, use, maintain, and troubleshoot the product. When custom question answering processes a manual, it extracts the headings and subheadings as questions and the subsequent content as answers. See an example [here](https://download.microsoft.com/download/2/9/B/29B20383-302C-4517-A006-B0186F04BE28/surface-pro-4-user-guide-EN.pdf).
+A manual is typically guidance material that accompanies a product. It helps the user to set up, use, maintain, and troubleshoot the product. When custom question answering processes a manual, it extracts the headings and subheadings as questions and the subsequent content as answers. For example, *see*, [Surface Book 3 Specs and Features guide](https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features).
 
 To follow is an example of a manual with an index page, and hierarchical content
 
@@ -50,21 +51,6 @@ To follow is an example of a semi-structured doc, without an index:
 > [!div class="mx-imgBorder"]
 > ![Azure Blob storage semi-structured Doc](../media/qnamaker-concepts-datasources/semi-structured-doc.png)
 
-### Unstructured document support
-
-Custom question answering now supports unstructured documents. A  document that doesn't have its content organized in a hierarchical manner, is missing a set structure or has its content free flowing can be considered as an unstructured document.
-
-To follow is an example of an unstructured PDF document:
-
-> [!div class="mx-imgBorder"]
-> ![Unstructured  document example for a project](../media/qnamaker-concepts-datasources/unstructured-qna-pdf.png)
-
-> [!NOTE]
-> QnA pairs aren't extracted in the "Edit sources" tab for unstructured sources.
-
-> [!IMPORTANT]
-> Support for unstructured file/content is available only in custom question answering.
-
 ### Structured custom question answering document
 
 The format for structured question-answers in DOC files is in the form of alternating questions and answers per line. It's one question per line followed by its answer in the following line, as shown:
@@ -112,7 +98,7 @@ Importing a project replaces the content of the existing project. Import require
 * First character of heading must be capitalized.
 * Don't end a heading with a question mark, `?`.
 
-**Sample documents**:<br>[Surface Pro (docx)](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/qna-maker/data-source-formats/multi-turn.docx)<br>[Contoso Benefits (docx)](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/qna-maker/data-source-formats/Multiturn-ContosoBenefits.docx)<br>[Contoso Benefits (pdf)](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/qna-maker/data-source-formats/Multiturn-ContosoBenefits.pdf)
+**Sample documents**:<br>[Surface Pro (docx)](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/qna-maker/data-source-formats/multi-turn.docx)<br>[Contoso Benefits (docx)](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/qna-maker/data-source-formats/Multiturn-ContosoBenefits.docx)<br>[Surface Book 3 (web)](https://support.microsoft.com/en-US/surface/models/surface-book-3-specs-and-features)
 
 ## FAQ URLs
 

@@ -40,29 +40,19 @@ items:
       href: personally-identifiable-information/quickstart.md
     - name: Language support
       href: personally-identifiable-information/language-support.md
-    - name: Responsible use of AI
-      items:
-      - name: Transparency note for PII
-        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-personally-identifiable-information.md
-        displayName: Transparency note for PII, Personally Identifiable Information transparency, Responsible AI, Responsible use of AI
-      - name: Integration and responsible use
-        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
-        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
-      - name: Data, privacy, and security
-        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
-        displayName: Data privacy, logging, data retention
-    - name: How-to guides
+
+    - name: Text PII
       items:
-      - name: Redact PII from text
-        href: personally-identifiable-information/how-to/redact-text-pii.md
-      - name: Redact PII from conversations
-        href: personally-identifiable-information/how-to/redact-conversation-pii.md
-      - name: Redact PII from native documents
-        href: personally-identifiable-information/how-to/redact-document-pii.md
-      - name:  Adapt PII to your domain
-        href: personally-identifiable-information/how-to/adapt-to-domain-pii.md
-      - name: Use Docker containers
+      - name: Overview
+        href: personally-identifiable-information/text-pii-overview.md
+      - name: How-to guides
         items:
+        - name: Detect and redact PII in text
+          href: personally-identifiable-information/how-to/redact-text-pii.md
+        - name: Adapt PII to your domain
+          href: personally-identifiable-information/how-to/adapt-to-domain-pii.md
+        - name: Use Docker containers
+          items:
           - name: Install and run containers
             href: personally-identifiable-information/how-to/use-containers.md
           - name: Configure containers
@@ -73,21 +63,53 @@ items:
             href: ../containers/disconnected-containers.md
           - name: Azure AI containers overview
             href: ../cognitive-services-container-support.md
-    - name: Recognized entity categories
-      items:
-      - name: PII in text
+      - name: Recognized entity categories
         items:
         - name: Extended format
           href: personally-identifiable-information/concepts/entity-categories.md
         - name: List format
           href: personally-identifiable-information/concepts/entity-categories-list.md
-      - name: PII in conversations
+
+    - name: Conversation PII
+      items:
+      - name: Overview
+        href: personally-identifiable-information/conversation-pii-overview.md
+      - name: How-to guides
+        items:
+        - name: Detect and redact PII in conversations
+          href: personally-identifiable-information/how-to/redact-conversation-pii.md
+      - name: Recognized entity categories
         items:
         - name: Extended format
           href: personally-identifiable-information/concepts/conversations-entity-categories.md
         - name: List format
           href: personally-identifiable-information/concepts/conversations-entities-list.md
 
+    - name: Document-based PII
+      items:
+      - name: Overview
+        href: personally-identifiable-information/document-based-pii-overview.md
+      - name: How-to guides
+        items:
+        - name: Detect and redact PII in native documents
+          href: personally-identifiable-information/how-to/redact-document-pii.md
+        - name: Create SAS tokens for storage containers
+          href: native-document-support/shared-access-signatures.md
+        - name: Create a managed identity for storage containers
+          href: native-document-support/managed-identities.md
+
+    - name: Responsible use of AI
+      items:
+      - name: Transparency note for PII
+        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-personally-identifiable-information.md
+        displayName: Transparency note for PII, Personally Identifiable Information transparency, Responsible AI, Responsible use of AI
+      - name: Integration and responsible use
+        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
+        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
+      - name: Data, privacy, and security
+        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
+        displayName: Data privacy, logging, data retention
+
   - name: Language detection
     items:
     - name: Overview
@@ -807,14 +829,6 @@ items:
     href: tutorials/use-kubernetes-service.md
   - name: Use language in Power Automate flows
     href: tutorials/power-automate.md
-  - name: Native document support
-    items:
-    - name: Native documents for language processing
-      href: native-document-support/overview.md
-    - name: Create SAS tokens for storage containers
-      href: native-document-support/shared-access-signatures.md
-    - name: Create a managed identity for storage containers
-      href: native-document-support/managed-identities.md
   - name: Scenario deep-dives
     items:
       - name: Call Center