@@ -250,24 +250,24 @@ Now that you've seen several patterns for one-to-many indexings, lets compare ke
 
 - To avoid creating parent search documents and ensuring the index contains only child documents of a uniform grain, set the `targetIndexName` for both the indexer definition and the selector to the same index, but add an extra `parameters` object after `selectors`, with a `projectionMode` key set to `skipIndexingParentDocuments`, as shown here:
 
-    ```json
-    "indexProjections": {
-        "selectors": [
-            ...
-        ],
-        "parameters": {
-            "projectionMode": "skipIndexingParentDocuments"
-        }
-    }
-    ```
+   ```json
+   "indexProjections": {
+       "selectors": [
+           ...
+       ],
+       "parameters": {
+           "projectionMode": "skipIndexingParentDocuments"
+       }
+   }
+   ```
 
 ## Review field mappings
 
 Indexers are affiliated with three different types of field mappings. Before you run the indexer, check your field mappings and know when to use each type.
 
 [Field mappings](search-indexer-field-mappings.md) are defined in an indexer and used to map a source field to an index field. Field mappings are used for data paths that lift data from the source and pass it in for indexing, with no intermediate skills processing step. Typically, an indexer can automatically map fields that have the same name and type. Explicit field mappings are only required when there's discrepancies. In one-to-many indexing and the patterns discussed thus far, you might not need field mappings.
 
-[Output field mappings](cognitive-search-output-field-mapping.md) are defined in an indexer and used to map enriched content generated by a skillset to a field into the main index. In the one-to-many patterns covered in this article, this is the parent index in a [two-index solution](#example-of-separate-parent-child-indexes). In the examples shown in this article, the parent index is sparse, with just a title field, and that field isn't populated with content from the skillset processing, so we don't an output field mapping.
+[Output field mappings](cognitive-search-output-field-mapping.md) are defined in an indexer and used to map enriched content generated by a skillset to a field into the main index. In the one-to-many patterns covered in this article, it is a single index schema that includes parent and child fields. The parent index is sparse, with just a title field. The title field isn't populated with content from the skillset processing, so we don't need an output field mapping.
 
 Indexer projection field mappings are used to map skillset-generated content to fields in the child index. In cases where the child index also includes parent fields (as in the [consolidated index solution](#single-index-schema-inclusive-of-parent-and-child-fields)), you should set up field mappings for every field that has content, including the parent-level title field, assuming you want the title to show up in each chunked document. If you're using [separate parent and child indexes](#example-of-separate-parent-child-indexes), the indexer projections should have field mappings for just the child-level fields.
 
@@ -409,4 +409,4 @@ The indexer definition specifies the components of the pipeline. In the indexer
 Data chunking and one-to-many indexing are part of the RAG pattern in Azure AI Search. Continue on to the following tutorial and code sample to learn more about it.
 
 > [!div class="nextstepaction"]
-> [How to build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md)
\ No newline at end of file
+> [How to build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md)

@@ -10,7 +10,7 @@ ms.update-cycle: 180-days
 ms.custom:
   - ignite-2024
 ms.topic: overview
-ms.date: 12/04/2025
+ms.date: 12/10/2025
 ---
 
 # What is Azure AI Search?
@@ -83,7 +83,7 @@ During indexing, you can use [AI enrichment](cognitive-search-concept-intro.md)
 
 ---
 
-:::image type="content" source="media/search-what-is-azure-search/azure-search.svg" alt-text="Diagram of the Azure AI Search architecture for classic search." lightbox="media/search-what-is-azure-search/azure-search.svg" :::
+:::image type="content" source="media/search-what-is-azure-search/classic-search-architecture.png" alt-text="Diagram of the Azure AI Search architecture for classic search." lightbox="media/search-what-is-azure-search/classic-search-architecture.png" :::
 
 > [!NOTE]
 > This diagram separates the indexing and query engines for clarity, but in Azure AI Search, they're the same component operating in read-write and read-only modes.
@@ -92,13 +92,11 @@ During indexing, you can use [AI enrichment](cognitive-search-concept-intro.md)
 
 [Agentic retrieval](agentic-retrieval-overview.md) is a multi-query pipeline designed for complex agent-to-agent workflows. Each query targets a [knowledge base](agentic-retrieval-how-to-create-knowledge-base.md) that represents a complete domain of knowledge. Your agent references the knowledge base for *what* to ground on, while the knowledge base handles *how* to perform grounding.
 
-One knowledge base consists of one or more [knowledge sources](agentic-knowledge-source-overview.md), an optional LLM for query planning and answer synthesis, and parameters that govern retrieval behavior. Each query undergoes planning, decomposition into focused subqueries, parallel retrieval from knowledge sources, semantic reranking, and results merging. The three-pronged response is optimized for agent consumption.
+A knowledge base consists of one or more [knowledge sources](agentic-knowledge-source-overview.md), an optional LLM for query planning and answer synthesis, and parameters that govern retrieval behavior. Each query undergoes planning, decomposition into focused subqueries, parallel retrieval from knowledge sources, semantic reranking, and results merging. The three-pronged response is optimized for agent consumption.
 
 Under the hood, agentic retrieval builds on the classic search architecture by adding a context layer (knowledge base) that orchestrates multi-source retrieval. Knowledge sources can be indexed or remote: indexed sources use the same indexing and query engines as classic search, while remote sources bypass indexing and are queried live.
 
-<!--
-:::image type="content" source="media/search-what-is-azure-search/azure-search-agentic-retrieval.svg" alt-text="Diagram of the Azure AI Search architecture for agentic retrieval." lightbox="media/search-what-is-azure-search/azure-search-agentic-retrieval.svg" :::
--->
+:::image type="content" source="media/search-what-is-azure-search/agentic-retrieval-architecture.png" alt-text="Diagram of the Azure AI Search architecture for agentic retrieval." lightbox="media/search-what-is-azure-search/agentic-retrieval-architecture.png" :::
 
 ## How they compare