Differentiate multimodal tutorials

HeidiSteen · HeidiSteen · commit c1650856c0c4 · 2025-07-28T15:06:49.000-07:00
diff --git a/articles/search/cognitive-search-skill-document-extraction.md b/articles/search/cognitive-search-skill-document-extraction.md
@@ -19,9 +19,9 @@ The **Document Extraction** skill extracts content from a file within the enrich
 
 For [vector](vector-search-overview.md) and [multimodal search](multimodal-search-overview.md), Document Extraction combined with the [Text Split skill](cognitive-search-skill-textsplit.md) is more affordable than other [data chunking approaches](vector-search-how-to-chunk-documents.md). The following tutorials demonstrate skill usage for different scenarios:
 
-+ [Tutorial: Index mixed content using multimodal embeddings and the Document Extraction skill](tutorial-document-extraction-multimodal-embeddings.md)
++ [Tutorial: Vectorize images and text](tutorial-document-extraction-multimodal-embeddings.md)
 
-+ [Tutorial: Index mixed content using image verbalizations and the Document Extraction skill](tutorial-document-extraction-image-verbalization.md)
++ [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md)
 
 > [!NOTE]
 > This skill isn't bound to Azure AI services and has no Azure AI services key requirement.
diff --git a/articles/search/cognitive-search-skill-document-intelligence-layout.md b/articles/search/cognitive-search-skill-document-intelligence-layout.md
@@ -22,16 +22,15 @@ The **Document Layout** skill analyzes a document to detect structure and charac
 
 This article is the reference documentation for the Document Layout skill. For usage information, see [How to chunk and vectorize by document layout](search-how-to-semantic-chunking.md). 
 
-It's common to use this skill on content such as PDFs that have structure and images. The following tutorials demonstrate several scenarios: 
+This skill uses the [Document Intelligence layout model](/azure/ai-services/document-intelligence/concept-layout) provided in [Azure AI Document Intelligence](/azure/ai-services/document-intelligence/overview).
 
-+ [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-document-layout-image-verbalization.md)
+This skill is bound to a [billable Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. Execution of built-in skills is charged at the existing [Azure AI services Standard price](https://azure.microsoft.com/pricing/details/cognitive-services/).
 
-+ [Tutorial: Index mixed content using multimodal embeddings and the Document Layout skill](tutorial-document-layout-multimodal-embeddings.md)
-
-> [!NOTE]
-> This skill uses the [Document Intelligence layout model](/azure/ai-services/document-intelligence/concept-layout) provided in [Azure AI Document Intelligence](/azure/ai-services/document-intelligence/overview).
+> [!TIP]
+> It's common to use this skill on content such as PDFs that have structure and images. The following tutorials demonstrate image verbalization with two different data chunking techniques:
 >
-> This skill is bound to a [billable Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. Execution of built-in skills is charged at the existing [Azure AI services Standard price](https://azure.microsoft.com/pricing/details/cognitive-services/).
+> - [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md)
+> - [Tutorial: Vectorize from a structured document layout](tutorial-document-layout-multimodal-embeddings.md)
 >
 
 ## Limitations
diff --git a/articles/search/cognitive-search-skill-genai-prompt.md b/articles/search/cognitive-search-skill-genai-prompt.md
@@ -17,7 +17,7 @@ ms.date: 07/28/2025
 
 The **GenAI (Generative AI) Prompt** skill executes a *chat completion* request against a Large Language Model (LLM) deployed in Azure AI Foundry or Azure OpenAI in Azure AI Foundry Models. Use this capability to create new information that can be indexed and stored as searchable content.
 
-Here are some examples of content generation:
+Here are some examples of how the GenAI prompt skill can help you create content:
 
 - verbalize images
 - summarize large passages of text
@@ -27,10 +27,10 @@ Here are some examples of content generation:
 The GenAI Prompt skill is available in the [2025-05-01-preview REST API](/rest/api/searchservice/skillsets/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) only. The skill supports text, image, and multimodal content such as a PDF that contains text and images. 
 
 > [!TIP]
-> It's common to use this skill combined with a data chunking skill. The following tutorials demonstrate the image verbalization scenarios with two different data chunking techniques:
+> It's common to use this skill combined with a data chunking skill. The following tutorials demonstrate image verbalization with two different data chunking techniques:
 >
-> - [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-document-layout-image-verbalization.md)
-> - [Tutorial: Index mixed content using image verbalizations and the Document Extraction skill](tutorial-document-extraction-image-verbalization.md)
+> - [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md)
+> - [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md)
 >
 
 ## Supported models
diff --git a/articles/search/multimodal-search-overview.md b/articles/search/multimodal-search-overview.md
@@ -116,8 +116,8 @@ To help you get started with multimodal search in Azure AI Search, here's a coll
 | Content | Description |
 |--|--|
 | [Quickstart: Multimodal search in the Azure portal](search-get-started-portal-image-search.md) | Create and test a multimodal index in the Azure portal using the wizard and Search Explorer. |
-| [Tutorial: Image verbalization and Document Extraction skill](tutorial-document-extraction-image-verbalization.md) | Extract text and images, verbalize diagrams, and embed the resulting descriptions and text into a searchable index. |
-| [Tutorial: Multimodal embeddings and Document Extraction skill](tutorial-document-extraction-multimodal-embeddings.md) | Use a vision-text model to embed both text and images directly, enabling visual-similarity search over scanned PDFs. |
-| [Tutorial: Image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md) | Apply layout-aware chunking and diagram verbalization, capture location metadata, and store cropped images for precise citations and page highlights. |
-| [Tutorial: Multimodal embeddings and Document Layout skill](tutorial-document-layout-multimodal-embeddings.md) | Combine layout-aware chunking with unified embeddings for hybrid semantic and keyword search that returns exact hit locations. |
+| [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md) | Extract text and images, verbalize diagrams, and embed the resulting descriptions and text into a searchable index. |
+| [Tutorial: Vectorize images and text](tutorial-document-extraction-multimodal-embeddings.md) | Use a vision-text model to embed both text and images directly, enabling visual-similarity search over scanned PDFs. |
+| [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md) | Apply layout-aware chunking and diagram verbalization, capture location metadata, and store cropped images for precise citations and page highlights. |
+| [Tutorial: Vectorize from a structured document layout](tutorial-document-layout-multimodal-embeddings.md) | Combine layout-aware chunking with unified embeddings for hybrid semantic and keyword search that returns exact hit locations. |
 | [Sample app: Multimodal RAG GitHub repository](https://aka.ms/azs-multimodal-sample-app-repo) | An end-to-end, code-ready RAG application with multimodal capabilities that surfaces both text snippets and image annotations. Ideal for jump-starting enterprise copilots. |
diff --git a/articles/search/search-get-started-portal-image-search.md b/articles/search/search-get-started-portal-image-search.md
@@ -465,7 +465,7 @@ This quickstart uses billable Azure resources. If you no longer need the resourc
 
 This quickstart introduced you to the **Import and vectorize data** wizard, which creates all of the necessary objects for multimodal search. To explore each step in detail, see the following tutorials:
 
-+ [Tutorial: Image verbalization and Document Extraction skill](tutorial-document-extraction-image-verbalization.md)
-+ [Tutorial: Image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md)
-+ [Tutorial: Multimodal embeddings and Document Extraction skill](tutorial-document-extraction-multimodal-embeddings.md)
-+ [Tutorial: Multimodal embeddings and Document Layout skill](tutorial-document-layout-multimodal-embeddings.md)
++ [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md)
++ [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md)
++ [Tutorial: Vectorize images and text](tutorial-document-extraction-multimodal-embeddings.md)
++ [Tutorial: Vectorize from a structured document layout](tutorial-document-layout-multimodal-embeddings.md)
diff --git a/articles/search/toc.yml b/articles/search/toc.yml
@@ -138,24 +138,14 @@ items:
       href: tutorial-adls-gen2-indexer-acls.md
   - name: Multimodal indexing tutorials
     items:
-    - name: Vectorize from any document
+    - name: Vectorize images and text
       href: tutorial-document-extraction-multimodal-embeddings.md
-    - name: Vectorize from a structured document
-      href: tutorial-document-layout-multimodal-embeddings.md
-    - name: Verbalize images from any document
+    - name: Verbalize images using generative AI
       href: tutorial-document-extraction-image-verbalization.md
-    - name: Verbalize images from a structured document
-      href: tutorial-document-layout-image-verbalization.md
-  - name: Multimodal indexing tutorials
-    items:
-    - name: Vectorize images and text
-      href: 
-    - name: Verbalize images
-      href: 
     - name: Vectorize from a structured document layout
-      href: 
+      href: tutorial-document-layout-multimodal-embeddings.md
     - name: Verbalize images from a structured document layout
-      href:     
+      href: tutorial-document-layout-image-verbalization.md
   - name: RAG tutorials
     items:
     - name: Build a RAG solution
diff --git a/articles/search/tutorial-document-extraction-image-verbalization.md b/articles/search/tutorial-document-extraction-image-verbalization.md
@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Use Image Verbalization and Document Extraction Skill for Multimodal Indexing'
+title: 'Tutorial: Verbalize images using generative AI'
 titleSuffix: Azure AI Search
 description: Learn how to extract, index, and search multimodal content using the Document Extraction skill for chunking and GenAI Prompt skill for image verbalizations.
 
@@ -14,7 +14,7 @@ ms.date: 05/29/2025
 
 ---
 
-# Tutorial: Index mixed content using image verbalizations and the Document Extraction skill
+# Tutorial: Verbalize images using generative AI
 
 Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline by describing visual content in natural language and embedding it alongside document text.
 
@@ -32,7 +32,7 @@ In this tutorial, you use:
 
 This tutorial demonstrates a lower-cost approach for indexing multimodal content using Document Extraction skill and image captioning. It enables extraction and search over both text and images from documents in Azure Blob Storage. However, it doesn't include locational metadata for text, such as page numbers or bounding regions.
 
-For a more comprehensive solution that includes structured text layout and spatial metadata, see [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md).
+For a more comprehensive solution that includes structured text layout and spatial metadata, see [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md).
 
 > [!NOTE]
 > Setting `imageAction` to `generateNormalizedImages` is required for this tutorial and incurs an additional charge for image extraction according to [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/).
@@ -752,4 +752,4 @@ Now that you're familiar with a sample implementation of a multimodal indexing s
 * [GenAI Prompt skill](cognitive-search-skill-genai-prompt.md)
 * [Vectors in Azure AI Search](vector-search-overview.md)
 * [Semantic ranking in Azure AI Search](semantic-search-overview.md)
-* [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md)
+* [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md)
diff --git a/articles/search/tutorial-document-extraction-multimodal-embeddings.md b/articles/search/tutorial-document-extraction-multimodal-embeddings.md
@@ -30,7 +30,7 @@ In this tutorial, you use:
 
 This tutorial demonstrates a lower-cost approach for indexing multimodal content using Document Extraction skill and image captioning. It enables extraction and search over both text and images from documents in Azure Blob Storage. However, it doesn't include locational metadata for text, such as page numbers or bounding regions.
 
-For a more comprehensive solution that includes structured text layout and spatial metadata, see [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md).
+For a more comprehensive solution that includes structured text layout and spatial metadata, see [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md).
 
 > [!NOTE]
 > Setting `imageAction` to `generateNormalizedImages` results in image extraction, which is an extra charge. For more information, see [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/) for image extraction pricing.
@@ -710,4 +710,4 @@ Now that you're familiar with a sample implementation of a multimodal indexing s
 * [AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md)
 * [Vectors in Azure AI Search](vector-search-overview.md)
 * [Semantic ranking in Azure AI Search](semantic-search-overview.md)
-* [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md)
+* [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md)
diff --git a/articles/search/tutorial-document-layout-image-verbalization.md b/articles/search/tutorial-document-layout-image-verbalization.md
@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Use Image Verbalization and Document Layout Skill for Multimodal Indexing'
+title: 'Tutorial: Verbalize images from a structured document layout'
 titleSuffix: Azure AI Search
 description: Learn how to extract, index, and search multimodal content using the Document Layout skill for chunking and GenAI Prompt skill for image verbalizations.
 
@@ -14,7 +14,7 @@ ms.date: 05/29/2025
 
 ---
 
-# Tutorial: Index mixed content using image verbalizations and the Document Layout skill
+# Tutorial: Verbalize images from a structured document layout
 
 In this Azure AI Search tutorial, learn how to build a multimodal indexing pipeline that chunks data based on document structure and uses image verbalization to describe images. Cropped images are stored in a knowledge store, and visual content is described in natural language and ingested alongside text in a searchable index.
 
@@ -26,7 +26,7 @@ In this tutorial, you use:
 
 + The [Document Layout skill (preview)](cognitive-search-skill-document-intelligence-layout.md) for extracting text and normalized images with its locationMetadata from various documents, such as page numbers or bounding regions.
 
-  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, is bound to Azure AI services, and requires a [billable resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. For a lower-cost solution to indexing multimodal content, see [Index multimodal content using image verbalization and Document Extraction skill](tutorial-document-extraction-image-verbalization.md).
+  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, is bound to Azure AI services, and requires a [billable resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. For a lower-cost solution to indexing multimodal content, see [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md).
 
 + The [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) to generate image captions, which are text-based descriptions of visual content, for search and grounding.
 
diff --git a/articles/search/tutorial-document-layout-multimodal-embeddings.md b/articles/search/tutorial-document-layout-multimodal-embeddings.md
@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Use Multimodal Embeddings and Document Layout Skill for Multimodal Indexing'
+title: 'Tutorial: Vectorize from a structured document layout'
 titleSuffix: Azure AI Search
 description: Learn how to extract, index, and search multimodal content using the Document Layout skill for chunking and Azure AI Vision for embeddings.
 
@@ -14,7 +14,7 @@ ms.date: 06/11/2025
 
 ---
 
-# Tutorial: Index mixed content using multimodal embeddings and the Document Layout skill
+# Tutorial: Vectorize from a structured document layout
 
 <!-- Multimodal plays an essential role in generative AI apps and the user experience as it enables the extraction of information not only from text but also from complex images embedded within documents.  -->
 In this Azure AI Search tutorial, learn how to build a multimodal indexing pipeline that chunks data based on document structure, and uses a multimodal embedding model to vectorize text and images in a searchable index.
@@ -25,7 +25,7 @@ In this tutorial, you use:
 
 + The [Document Layout skill (preview)](cognitive-search-skill-document-intelligence-layout.md) for extracting text and normalized images with its locationMetadata from various documents, such as page numbers or bounding regions.
 
-  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, is bound to Azure AI services, and requires a [billable resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. For a lower-cost solution to indexing multimodal content, see [Index multimodal content using image verbalization and Document Extraction skill](tutorial-document-extraction-image-verbalization.md).
+  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, is bound to Azure AI services, and requires a [billable resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. For a lower-cost solution to indexing multimodal content, see [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md).
 
 + Vectorization using the [Azure AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md), which generates embeddings for both text and images.
 
@@ -614,4 +614,4 @@ Now that you're familiar with a sample implementation of a multimodal indexing s
 + [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md)
 + [Vectors in Azure AI Search](vector-search-overview.md)
 + [Semantic ranking in Azure AI Search](semantic-search-overview.md)
-+ [Index multimodal content using embeddings and Document Extraction skill](tutorial-document-extraction-multimodal-embeddings.md)
++ [Tutorial: Vectorize images and text](tutorial-document-extraction-multimodal-embeddings.md)
