@@ -3,11 +3,12 @@ title: 'Tutorial: Debug Skillsets'
 description: Practice creating and completing a debug session on an Azure AI Search skillset. This tutorial provides a buggy sample skillset that you resolve in a debug session.
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 03/31/2025
+ms.date: 04/27/2026
 ms.update-cycle: 365-days
 ms.custom:
   - ignite-2023
   - sfi-image-nochange
+ai-usage: ai-assisted
 ---
 
 # Tutorial: Fix a skillset using Debug Sessions

@@ -5,7 +5,7 @@ summary: Learn how to use Azure AI Search for information retrieval at scale, wi
 metadata:
   title: Azure AI Search documentation
   description: Learn how to use Azure AI Search for information retrieval at scale, with support for text, vector, and multimodal content in traditional and generative search scenarios.
-  ms.date: 12/12/2025
+  ms.date: 04/27/2026
   ms.service: azure-ai-search
   ms.topic: landing-page
   ms.custom:

@@ -8,7 +8,8 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: upgrade-and-migration-article
-ms.date: 03/30/2026
+ms.date: 04/27/2026
+ai-usage: ai-assisted
 ---
 
 # Upgrade to the latest REST API in Azure AI Search

@@ -6,7 +6,8 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: concept-article
-ms.date: 03/30/2026
+ms.date: 04/27/2026
+ai-usage: ai-assisted
 ---
 
 # Preview features in Azure AI Search

@@ -3,9 +3,10 @@ title: "Quickstart: Search Explorer Query Tool"
 description: Search explorer is a query tool in the Azure portal that sends query requests to a search index in Azure AI Search. Use it to learn syntax, test query expressions, or inspect a search document.
 ms.service: azure-ai-search
 ms.topic: quickstart
-ms.date: 12/04/2025
+ms.date: 04/27/2026
 ms.custom:
   - mode-ui
+ai-usage: ai-assisted
 ---
 
 # Quickstart: Use Search explorer to run queries in the Azure portal
@@ -67,7 +68,7 @@ The following are examples of JSON queries you can run using Search explorer. To
 
 ### Run an unspecified query
 
-In Search explorer, POST requests are formulated internally using [Documents - Search Post (REST API)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true), with responses returned as verbose JSON documents.
+In Search explorer, POST requests are formulated internally using [Documents - Search Post](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2025-09-01&preserve-view=true) (REST API), with responses returned as verbose JSON documents.
 
 For a first look at content, execute an empty search by selecting **Search** with no terms provided. An empty search is useful as a first query because it returns entire documents so that you can review document composition. On an empty search, there's no search score, and documents are returned in arbitrary order (`"@search.score": 1` for all documents). By default, 50 documents are returned per search request.
 
@@ -195,7 +196,7 @@ In this quickstart, you used **Search explorer** to query an index using the RES
 
 ## Next step
 
-To learn more about query structures and syntax, use a REST client to create query expressions that use more parts of the REST API. [Documents - Search Post (REST API)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) is especially helpful for learning and exploration.
+To learn more about query structures and syntax, use a REST client to create query expressions that use more parts of the REST API. [Documents - Search Post](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2025-09-01&preserve-view=true) (REST API) is especially helpful for learning and exploration.
 
 > [!div class="nextstepaction"]
 > [Quickstart: Full-text search](search-get-started-text.md)

@@ -3,11 +3,12 @@ title: Text Query Filters
 description: Filter by user security identity, language, geo-location, or numeric values to reduce search results on queries in a search index in Azure AI Search.
 ms.service: azure-ai-search
 ms.topic: concept-article
-ms.date: 03/11/2025
+ms.date: 04/27/2026
 ms.update-cycle: 365-days
 ms.custom:
   - devx-track-csharp
   - ignite-2023
+ai-usage: ai-assisted
 ---
 
 # Filters for keyword search in Azure AI Search
@@ -26,7 +27,7 @@ Common scenarios include:
 
 + Slice search results based on content in the index. Given a schema with hotel location, categories, and amenities, you might create a filter to explicitly match on criteria (in Seattle, on the water, with a view). 
 
-+ Implement a search experience comes with a filter dependency:
++ Implement a search experience that comes with a filter dependency:
 
   + [Faceted navigation](search-faceted-navigation.md) uses a filter to pass back the facet category selected by the user.
   + [Geospatial search](search-query-odata-geo-spatial-functions.md) uses a filter to pass coordinates of the current location in "find near me" apps and functions that match within an area or by distance.
@@ -48,7 +49,7 @@ Filters are OData expressions, articulated in the [filter syntax](search-query-o
 
 You can specify one filter for each **search** operation, but the filter itself can include multiple fields, multiple criteria, and if you use an **`ismatch`** function, multiple full-text search expressions. In a multi-part filter expression, you can specify predicates in any order (subject to the rules of operator precedence). There's no appreciable gain in performance if you try to rearrange predicates in a particular sequence.
 
-One of the limits on a filter expression is the maximum size limit of the request. The entire request, inclusive of the filter, can be a maximum of 16 MB for POST, or 8 KB for GET. There's also a limit on the number of clauses in your filter expression. A good rule of thumb is that if you have hundreds of clauses, you are at risk of running into the limit. We recommend designing your application in such a way that it doesn't generate filters of unbounded size.
+One of the limits on a filter expression is the maximum size limit of the request. The entire request, inclusive of the filter, can be a maximum of 16 MB for POST, or 8 KB for GET. There's also a limit on the number of clauses in your filter expression. A good rule of thumb is that if you have hundreds of clauses, you risk hitting the limit. We recommend designing your application in such a way that it doesn't generate filters of unbounded size.
 
 The following examples represent prototypical filter definitions in several APIs.
 

@@ -3,11 +3,12 @@ title: Search Over CSV Blobs Using delimitedText Parsing
 description: Extract CSV blobs from Azure Blob Storage or Azure Files, and import as search documents into Azure AI Search using the delimitedText parsing mode.
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 03/11/2025
+ms.date: 04/27/2026
 ms.update-cycle: 365-days
 ms.custom:
   - ignite-2023
   - sfi-ropc-nochange
+ai-usage: ai-assisted
 ---
 
 # Index CSV blobs and files using delimitedText parsing mode

@@ -5,11 +5,12 @@ ms.devlang: rest-api
 ms.reviewer: gimondra
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 03/28/2025
+ms.date: 04/27/2026
 ms.update-cycle: 365-days
 ms.custom:
   - ignite-2023
   - sfi-image-nochange
+ai-usage: ai-assisted
 ---
 
 # Tutorial: Index and enrich encrypted blobs for full-text search in Azure AI Search

@@ -3,11 +3,12 @@ title: Run or Reset Indexers
 description: Run indexers in full, or reset an indexer, skills, or individual documents to refresh all or part of a search index or knowledge store.
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 03/26/2026
+ms.date: 04/27/2026
 ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
   - sfi-image-nochange
+ai-usage: ai-assisted
 ---
 
 # Run or reset indexers, skills, or documents
@@ -269,15 +270,15 @@ POST https://[service name].search.windows.net/indexers/[indexer name]/resetdocs
 ## How to resync indexers (preview)
 
 [Resync Indexers](/rest/api/searchservice/indexers/resync?view=rest-searchservice-2025-11-01-preview&preserve-view=true) is a preview REST API that performs a partial reindex of all documents.
-An indexer is considered synchronized with its data source when specific fields of all documents in the target index are consistent with the data in the data source. Typically, an indexer achieves synchronization after a successful initial run. If a document is deleted from the data source, the indexer remains synchronized according to this definition. However, during the next indexer run, the corresponding document in the target index will be removed if delete tracking is enabled.
+An indexer is considered synchronized with its data source when specific fields of all documents in the target index are consistent with the data in the data source. Typically, an indexer achieves synchronization after a successful initial run. If a document is deleted from the data source, the indexer remains synchronized according to this definition. However, during the next indexer run, the corresponding document in the target index is removed if delete tracking is enabled.
 
-If a document is modified in the data source, the indexer becomes unsynchronized. Generally, change tracking mechanisms will resynchronize the indexer during the next run. For example, in Azure Storage, modifying a blob updates its last modified time, allowing it to be re-indexed in the subsequent indexer run because the updated time surpasses the high-water mark set by the previous run.
+If a document is modified in the data source, the indexer becomes unsynchronized. Generally, change tracking mechanisms resynchronize the indexer during the next run. For example, in Azure Storage, modifying a blob updates its last modified time, allowing it to be re-indexed in the subsequent indexer run because the updated time surpasses the high-water mark set by the previous run.
 
-In contrast, for certain data sources like ADLS Gen2, altering the access control lists (ACLs) of a blob does not change its last modified time, rendering change tracking ineffective if ACLs are to be ingested. Consequently, the modified blob will not be re-indexed in the subsequent run, as only documents modified after the last high-water mark are processed.
+In contrast, for certain data sources like ADLS Gen2, altering the access control lists (ACLs) of a blob does not change its last modified time, rendering change tracking ineffective if ACLs are to be ingested. Consequently, the modified blob isn't re-indexed in the subsequent run, as only documents modified after the last high-water mark are processed.
 
 While using either "reset" or "reset docs" can address this issue, "reset" can be time-consuming and inefficient for large datasets, and "reset docs" requires identifying the document key of the blob intended for update.
 
-Resync Indexers offers an efficient and convenient alternative. Users simply place the indexer in resync mode and specify the content to resynchronize by calling the resync indexers API. In the next run, the indexer will inspect only relevant portion of data in the source and avoid any unnecessary processing that is unrelated to the specified data.  It will also query the existing documents in the target index and only update the documents that show discrepancies between the data source and the target index. After the resync run, the indexer will be synchronized and revert to regular indexer run mode for subsequent runs.
+Resync Indexers offers an efficient and convenient alternative. Users simply place the indexer in resync mode and specify the content to resynchronize by calling the resync indexers API. In the next run, the indexer inspects only the relevant portion of data in the source and avoids any unnecessary processing that is unrelated to the specified data. It also queries the existing documents in the target index and only updates the documents that show discrepancies between the data source and the target index. After the resync run, the indexer is synchronized and reverts to regular indexer run mode for subsequent runs.
 
 ### How to resync and run indexers
 

@@ -5,8 +5,9 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 09/23/2025
+ms.date: 04/27/2026
 ms.update-cycle: 180-days
+ai-usage: ai-assisted
 ---
 
 # Field mappings and transformations using Azure AI Search indexers
@@ -36,7 +37,7 @@ Make sure you're using a [supported data source](search-indexer-overview.md#supp
 | Use-case | Description |
 |----------|-------------|
 | Name discrepancy | Suppose your data source has a field named `_city`. Given that Azure AI Search doesn't allow field names that start with an underscore, a field mapping lets you effectively map "_city" to "city". </p>If your indexing requirements include retrieving content from multiple data sources, where field names vary among the sources, you could use a field mapping to clarify the path.|
-| Type discrepancy | Supposed you want a source integer field to be of type `Edm.String` so that it's searchable in the search index. Because the types are different, you'll need to define a field mapping in order for the data path to succeed. Note that Azure AI Search has a smaller set of [supported data types](/rest/api/searchservice/supported-data-types) than many data sources. If you're importing SQL data, a field mapping allows you to [map the SQL data type](search-how-to-index-sql-database.md#mapping-data-types) you want in a search index.|
+| Type discrepancy | Suppose you want a source integer field to be of type `Edm.String` so that it's searchable in the search index. Because the types are different, you need to define a field mapping in order for the data path to succeed. Note that Azure AI Search has a smaller set of [supported data types](/rest/api/searchservice/supported-data-types) than many data sources. If you're importing SQL data, a field mapping allows you to [map the SQL data type](search-how-to-index-sql-database.md#mapping-data-types) you want in a search index.|
 | One-to-many data paths | You can populate multiple fields in the index with content from the same source field. For example, you might want to apply different analyzers to each field to support different use cases in your client app.|
 | Encoding and decoding | You can apply [mapping functions](#mappingFunctions) to support Base64 encoding or decoding of data during indexing. |
 | Split strings or recast arrays into collections | You can apply [mapping functions](#mappingFunctions) to split a string that includes a delimiter, or to send a JSON array to a search field of type `Collection(Edm.String)`.

@@ -1,13 +1,14 @@
 ---
 title: Full-Text Search
-description: Describes concepts and architecture of query processing and document retrieval for full text search, as implemented Azure AI Search.
+description: Describes concepts and architecture of query processing and document retrieval for full text search, as implemented in Azure AI Search.
 ms.reviewer: jlembicz
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 03/07/2025
+ms.date: 04/27/2026
 ms.update-cycle: 365-days
+ai-usage: ai-assisted
 ---
 
 # Full-text search in Azure AI Search
@@ -373,7 +374,7 @@ There are two ways to tune relevance scores in Azure AI Search:
 
 All indexes in Azure AI Search are automatically split into multiple shards, allowing us to quickly distribute the index among multiple nodes during service scale up or scale down. When a search request is issued, it's issued against each shard independently. The results from each shard are then merged and ordered by score (if no other ordering is defined). It's important to know that the scoring function weights query term frequency against its inverse document frequency in all documents within the shard, not across all shards!
 
-This means a relevance score *could* be different for identical documents if they reside on different shards. Fortunately, such differences tend to disappear as the number of documents in the index grows due to more even term distribution. It's not possible to assume on which shard any given document will be placed. However, assuming a document key doesn't change, it will always be assigned to the same shard.
+This means a relevance score *could* be different for identical documents if they reside on different shards. Fortunately, such differences tend to disappear as the number of documents in the index grows due to more even term distribution. It's not possible to assume on which shard any given document will be placed. However, assuming a document key doesn't change, it's always assigned to the same shard.
 
 In general, document score isn't the best attribute for ordering documents if order stability is important. For example, given two documents with an identical score, there's no guarantee that one appears first in subsequent runs of the same query. Document score should only give a general sense of document relevance relative to other documents in the results set.
 

@@ -5,8 +5,9 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 03/28/2025
+ms.date: 04/27/2026
 ms.update-cycle: 365-days
+ai-usage: ai-assisted
 ---
 
 # Tutorial: Index nested JSON blobs from Azure Storage using REST

@@ -5,8 +5,9 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 03/28/2025
+ms.date: 04/27/2026
 ms.update-cycle: 365-days
+ai-usage: ai-assisted
 ---
 
 # Tutorial: Index large data from Apache Spark using SynapseML and Azure AI Search

@@ -4,7 +4,8 @@ description: Learn how to combine scoring profiles with semantic ranking in Azur
 ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.topic: how-to
-ms.date: 09/28/2025
+ms.date: 04/27/2026
+ai-usage: ai-assisted
 ---
 
 # Use scoring profiles with semantic ranker in Azure AI Search

@@ -5,12 +5,13 @@ ms.reviewer: diberry
 ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.topic: tutorial
-ms.date: 09/23/2025
+ms.date: 04/27/2026
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
   - ignite-2023
 ms.devlang: csharp
+ai-usage: ai-assisted
 ---
 
 # Step 3 - Deploy the search-enabled .NET website

@@ -5,12 +5,13 @@ ms.reviewer: diberry
 ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.topic: tutorial
-ms.date: 09/23/2025
+ms.date: 04/27/2026
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
   - ignite-2023
 ms.devlang: csharp
+ai-usage: ai-assisted
 ---
 
 # Step 1 - Overview of adding search to a static web app with .NET

@@ -5,12 +5,13 @@ ms.reviewer: diberry
 ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.topic: tutorial
-ms.date: 09/23/2025
+ms.date: 04/27/2026
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
   - ignite-2023
 ms.devlang: csharp
+ai-usage: ai-assisted
 ---
 
 # Step 4 - Explore the .NET search code

@@ -4,9 +4,10 @@ description: Learn how to add filter expressions to vector queries in Azure AI S
 ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.custom:
-  + ignite-2023
+  - ignite-2023
 ms.topic: how-to
-ms.date: 09/16/2025
+ms.date: 04/27/2026
+ai-usage: ai-assisted
 ---
 
 # Add a filter to a vector query in Azure AI Search
@@ -192,7 +193,7 @@ To understand the conditions under which one filter mode performs better than th
 
 For the small and medium workloads, we used a Standard 2 (S2) service with one partition and one replica. For the large workload, we used a Standard 3 (S3) service with 12 partitions and one replica.
 
-Indexes had an identical construction: one key field, one vector field, one text field, and one numeric filterable field. The following index is defined using the `2023-11-03` syntax.
+Indexes had an identical construction: one key field, one vector field, one text field, and one numeric filterable field. The following index is defined using the `2023-11-01` syntax.
 
 ```python
 def get_index_schema(self, index_name, dimensions):

@@ -6,7 +6,8 @@ ms.update-cycle: 180-days
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 09/28/2025
+ms.date: 04/27/2026
+ai-usage: ai-assisted
 ---
 
 # Compress vectors using scalar or binary quantization

@@ -6,7 +6,8 @@ ms.update-cycle: 180-days
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 09/29/2025
+ms.date: 04/27/2026
+ai-usage: ai-assisted
 ---
 
 # Create a vector query in Azure AI Search
@@ -499,12 +500,12 @@ Vector weighting applies to vectors only. The text query in this example, `"hell
 
 Because nearest neighbor search always returns the requested `k` neighbors, it's possible to get multiple low-scoring matches as part of meeting the `k` number requirement on search results. To exclude low-scoring search results, you can add a `threshold` query parameter that filters out results based on a minimum score. Filtering occurs before [fusing results](hybrid-search-ranking.md) from different recall sets.
 
-This parameter is in preview. We recommend the  [2024-05-01-preview](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) REST API version.
+This parameter is in preview. We recommend the latest preview version of [Documents - Search Post](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2025-11-01-preview&preserve-view=true) (REST API).
 
 In this example, all matches that score below 0.8 are excluded from vector search results, even if the number of results falls below `k`.
 
 ```http
-POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-05-01-preview 
+POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2025-11-01-preview 
     Content-Type: application/json 
     api-key: [admin key] 
 

@@ -6,7 +6,8 @@ ms.update-cycle: 180-days
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 09/29/2025
+ms.date: 04/27/2026
+ai-usage: ai-assisted
 ---
 
 # Eliminate optional vector instances from storage
@@ -50,7 +51,7 @@ Considerations for setting `"stored": false`:
 For new vector fields in a search index, set `"stored": false` to permanently remove retrievable storage for the vector field. The following example shows a vector field definition with the `stored` property.
 
 ```http
-PUT https://[service-name].search.windows.net/indexes/demo-index?api-version=2025-09-01@search.rerankerBoostedScore 
+PUT https://[service-name].search.windows.net/indexes/demo-index?api-version=2025-09-01 
   Content-Type: application/json  
   api-key: [admin key]