@@ -185,7 +185,7 @@ Enrichments are billable operations. If you no longer need to call Azure AI serv
 
 Key-based billing applies when API calls to Azure AI services resources exceed 20 API calls per indexer, per day. 
 
-The key is used for billing, but not for enrichment operations' connections. For connections, a search service [connects over the internal network](search-security-overview.md#internal-traffic) to an Azure AI services resource that's located in the [same physical region](https://azure.microsoft.com/global-infrastructure/services/?products=search). Most regions that offer Azure AI Search also offer other Azure AI services such as Language. If you attempt AI enrichment in a region that doesn't have both services, you'll see this message: "Provided key isn't a valid CognitiveServices type key for the region of your search service."
+The key is used for billing, but not for enrichment operations' connections. For connections, a search service [connects over the internal network](search-security-overview.md#internal-traffic) to an Azure AI services resource that's located in the [same physical region](search-region-support.md). Most regions that offer Azure AI Search also offer other Azure AI services such as Language. If you attempt AI enrichment in a region that doesn't have both services, you'll see this message: "Provided key isn't a valid CognitiveServices type key for the region of your search service."
 
 Currently, billing for [built-in skills](cognitive-search-predefined-skills.md) requires a public connection from Azure AI Search to another Azure AI service. Disabling public network access breaks billing. If disabling public networks is a requirement, you can configure a [Custom Web API skill](cognitive-search-custom-skill-interface.md) implemented with an [Azure Function](cognitive-search-create-custom-skill-example.md) that supports [private endpoints](/azure/azure-functions/functions-create-vnet) and add the [Azure AI services resource to the same VNET](/azure/ai-services/cognitive-services-virtual-networks). In this way, you can call Azure AI services resource directly from the custom skill using private endpoints.
 

@@ -106,7 +106,7 @@ In Azure Storage, a [knowledge store](knowledge-store-concept-intro.md) can assu
 
 ## Availability and pricing
 
-Enrichment is available in regions that have Azure AI services. You can check the availability of enrichment on the [Azure products available by region](https://azure.microsoft.com/global-infrastructure/services/?products=search) page. 
+Enrichment is available in regions that have Azure AI services. You can check the availability of enrichment on the [regions list](search-region-support.md) page. 
 
 Billing follows a pay-as-you-go pricing model. The costs of using built-in skills are passed on when a multi-region Azure AI services key is specified in the skillset. There are also costs associated with image extraction, as metered by Azure AI Search. Text extraction and utility skills, however, aren't billable. For more information, see [How you're charged for Azure AI Search](search-sku-manage-costs.md#how-youre-charged-for-azure-ai-search).
 

@@ -57,7 +57,7 @@ Although double encryption is now available in all regions, support was rolled o
   + US Gov Virginia
   + US Gov Arizona
 
-+ The second rollout on May 13, 2021 added encryption for temporary disks and extended CMK encryption to [all supported regions](https://azure.microsoft.com/global-infrastructure/services/?products=search#select-product).
++ The second rollout on May 13, 2021 added encryption for temporary disks and extended CMK encryption to [all supported regions](search-region-support.md).
 
   If you're using CMK from a service created during the first rollout and you also want CMK encryption over temporary disks, you need to create a new search service in your region of choice and redeploy your content.
 

@@ -10,18 +10,18 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 08/05/2024
+ms.date: 09/24/2024
 ---
 
 # Configure semantic ranker and return captions in search results
 
-This article explains how to configure a search index for semantic reranking. 
-
 Semantic ranking iterates over an initial result set, applying an L2 ranking methodology that promotes the most semantically relevant results to the top of the stack. You can also get semantic captions, with highlights over the most relevant terms and phrases, and [semantic answers](semantic-answers.md).
 
+This article explains how to configure a search index for semantic reranking. 
+
 ## Prerequisites
 
-+ A search service on a basic tier or higher, subject to [region availability](https://azure.microsoft.com/global-infrastructure/services/?products=search).
++ A search service on a Basic tier or higher, subject to [region availability](search-region-support.md).
 
 + Semantic ranker [enabled on your search service](semantic-how-to-enable-disable.md).
 

@@ -10,16 +10,16 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 02/08/2024
+ms.date: 09/24/2024
 ---
 
 # Enable or disable semantic ranker
 
-Semantic ranker is a premium feature that's billed by usage. By default, semantic ranker is disabled on all services. 
+Semantic ranker is a premium feature that's billed by usage. By default, semantic ranker is turned off on a new search service, but it can be enabled by anyone with **Contributor** permissions. If you don't want anyone enabling it inadvertently, you can [disable it using the REST API](#disable-semantic-ranker-using-the-rest-api).
 
 ## Check availability
 
-Check the [Products Available by Region](https://azure.microsoft.com/global-infrastructure/services/?products=search) page on the Azure web site to see if your region is listed.
+Check the [regions list](search-region-support.md) to see if your region is listed.
 
 ## Enable semantic ranker
 
@@ -37,7 +37,7 @@ Follow these steps to enable [semantic ranker](semantic-search-overview.md) at t
 
    :::image type="content" source="media/semantic-search-overview/semantic-search-billing.png" alt-text="Screenshot of enabling semantic ranking in the Azure portal." border="true":::
 
-The free plan is capped at 1,000 queries per month. After the first 1,000 queries in the free plan, you'll receive an error message letting you know you've exhausted your quota the next time you issue a semantic query. When this happens, you need to upgrade to the standard plan to continue using semantic ranking.
+The free plan is capped at 1,000 queries per month. After the first 1,000 queries in the free plan, an error message indicates you've exhausted your quota on the next semantic query. When quota is exhausted, you should upgrade to the standard plan to continue using semantic ranking.
 
 ### [**REST**](#tab/enable-rest)
 
@@ -78,7 +78,7 @@ PATCH https://management.azure.com/subscriptions/{{subscriptionId}}/resourcegrou
     }
 ```
 
-To re-enable semantic ranker, rerun the above request, setting "semanticSearch" to either "free" (default) or "standard".
+To re-enable semantic ranker, rerun the previous request, setting "semanticSearch" to either "free" (default) or "standard".
 
 ## Next steps
 

@@ -10,18 +10,18 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 07/24/2024
+ms.date: 09/24/2024
 ---
 
 # Add semantic ranking to queries in Azure AI Search
 
-This article explains how to invoke the semantic ranker on queries. You can apply semantic ranking to text queries, hybrid queries, and vector queries if your search documents contain string fields and the [vector query has a text representation](vector-search-how-to-query.md#query-with-integrated-vectorization).
+You can apply semantic ranking to text queries, hybrid queries, and vector queries if your search documents contain string fields and the [vector query has a text representation](vector-search-how-to-query.md#query-with-integrated-vectorization) in the search document.
 
-Semantic ranker iterates over an initial result set, applying an L2 ranking methodology that promotes the most semantically relevant results to the top of the stack. You can also get semantic captions, with highlights over the most relevant terms and phrases, and [semantic answers](semantic-answers.md).
+This article explains how to invoke the semantic ranker on queries. 
 
 ## Prerequisites
 
-+ A search service, basic tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
++ A search service, Basic tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
 
 + An existing search index with a [semantic configuration](semantic-how-to-configure.md) and rich text content.
 
@@ -51,7 +51,7 @@ A few query capabilities bypass relevance scoring, which makes them incompatible
 
 ## Set up the query
 
-In this step, add parameters to the query request. To be successful, your query should be full text search (using the `search` parameter to pass in a string), and the index should contain text fields with rich semantic content and a semantic configuration.
+In this step, add parameters to the query request. To be successful, your query should be full text search (using the `search` parameter to pass in a string) or a vector query, and the index should contain text fields with rich semantic content and a semantic configuration.
 
 ### [**Azure portal**](#tab/portal-query)
 
@@ -189,9 +189,9 @@ Console.WriteLine($"Total number of search results:{count}");
 
 ## Evaluate the response
 
-Only the top 50 matches from the initial results can be semantically ranked. As with all queries, a response is composed of all fields marked as retrievable, or just those fields listed in the select parameter. A response includes the original relevance score, and might also include a count, or batched results, depending on how you formulated the request.
+Only the top 50 matches from the initial results can be semantically ranked. As with all queries, a response is composed of all fields marked as retrievable, or just those fields listed in the `select` parameter. A response includes the original relevance score, and might also include a count, or batched results, depending on how you formulated the request.
 
-In semantic ranking, the response has more elements: a new semantically ranked relevance score, an optional caption in plain text and with highlights, and an optional [answer](semantic-answers.md). If your results don't include these extra elements, then your query might be misconfigured. As a first step towards troubleshooting the problem, check the semantic configuration to ensure it's specified in both the index definition and query.
+In semantic ranking, the response has more elements: a new [semantically ranked relevance score](semantic-search-overview.md#how-ranking-is-scored), an optional caption in plain text and with highlights, and an optional [answer](semantic-answers.md). If your results don't include these extra elements, then your query might be misconfigured. As a first step towards troubleshooting the problem, check the semantic configuration to ensure it's specified in both the index definition and query.
 
 In a client app, you can structure the search page to include a caption as the description of the match, rather than the entire contents of a specific field. This approach is useful when individual fields are too dense for the search results page.
 

@@ -9,60 +9,56 @@ ms.author: heidist
 ms.service: cognitive-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 06/12/2024
+ms.topic: concept-article
+ms.date: 09/24/2024
 ---
 
 # Semantic ranking in Azure AI Search
 
-In Azure AI Search, *semantic ranker* is a feature that measurably improves search relevance by using Microsoft's language understanding models to rerank search results. This article is a high-level introduction. The section at the end covers [availability and pricing](#availability-and-pricing).
+In Azure AI Search, *semantic ranker* is a feature that measurably improves search relevance by using Microsoft's language understanding models to rerank search results. This article is a high-level introduction to help you understand the behaviors and benefits of semantic ranker.
 
-Semantic ranker is a premium feature, billed by usage. We recommend this article for background, but if you'd rather get started, follow these steps:
-
-> [!div class="checklist"]
-> * [Check regional availability](search-region-support.md)
-> * [Sign in to Azure portal](https://portal.azure.com) to verify your search service is Basic or higher
-> * [Enable semantic ranker and choose a pricing plan](semantic-how-to-enable-disable.md)
-> * [Configure semantic ranker in a search index](semantic-how-to-configure.md)
-> * [Set up queries to return semantic captions and highlights](semantic-how-to-query-request.md)
-> * [Optionally, return semantic answers](semantic-answers.md)
+Semantic ranker is a premium feature, billed by usage. We recommend this article for background, but if you'd rather get started, [follow these steps](#how-to-get-started-with-semantic-ranker).
 
 > [!NOTE]
-> Semantic ranker doesn't use generative AI or vectors. If you're looking for vector support and similarity search? See [Vector search in Azure AI Search](vector-search-overview.md) for details.
+> Semantic ranker doesn't use generative AI or vectors. If you're looking for vector support and similarity search, see [Vector search in Azure AI Search](vector-search-overview.md) for details.
 
 ## What is semantic ranking?
 
-Semantic ranker is a collection of query-side capabilities that improve the quality of an initial [BM25-ranked](index-similarity-and-scoring.md) or [RRF-ranked](hybrid-search-ranking.md) search result for text-based queries. When you enable it on your search service, semantic ranking extends the query execution pipeline in two ways: 
+Semantic ranker is a collection of query-side capabilities that improve the quality of an initial [BM25-ranked](index-similarity-and-scoring.md) or [RRF-ranked](hybrid-search-ranking.md) search result for text-based queries, vector queries, and hybrid queries. When you enable it on your search service, semantic ranking extends the query execution pipeline in two ways: 
 
-* First, it adds secondary ranking over an initial result set that was scored using BM25 or RRF. This secondary ranking uses multi-lingual, deep learning models adapted from Microsoft Bing to promote the most semantically relevant results. 
+* First, it adds secondary ranking over an initial result set that was scored using BM25 or Reciprocal Rank Fusion (RRF). This secondary ranking uses multi-lingual, deep learning models adapted from Microsoft Bing to promote the most semantically relevant results. 
 
 * Second, it extracts and returns captions and answers in the response, which you can render on a search page to improve the user's search experience.
 
 Here are the capabilities of the semantic reranker.
 
-| Feature | Description |
+| Capability | Description |
 |---------|-------------|
-| Semantic ranker | Uses the context or semantic meaning of a query to compute a new relevance score over preranked results. |
-| [Semantic captions and highlights](semantic-how-to-query-request.md) | Extracts verbatim sentences and phrases from a document that best summarize the content, with highlights over key passages for easy scanning. Captions that summarize a result are useful when individual content fields are too dense for the search results page. Highlighted text elevates the most relevant terms and phrases so that users can quickly determine why a match was considered relevant. |
+| L2 ranking | Uses the context or semantic meaning of a query to compute a new relevance score over preranked results. |
+| [Semantic captions and highlights](semantic-how-to-query-request.md) | Extracts verbatim sentences and phrases from fields that best summarize the content, with highlights over key passages for easy scanning. Captions that summarize a result are useful when individual content fields are too dense for the search results page. Highlighted text elevates the most relevant terms and phrases so that users can quickly determine why a match was considered relevant. |
 | [Semantic answers](semantic-answers.md) | An optional and extra substructure returned from a semantic query. It provides a direct answer to a query that looks like a question. It requires that a document has text with the characteristics of an answer. |
 
 ## How semantic ranker works
 
-Semantic ranking feeds a query and results to language understanding models hosted by Microsoft and scans for better matches. 
+Semantic ranker feeds a query and results to language understanding models hosted by Microsoft and scans for better matches. 
 
 The following illustration explains the concept. Consider the term "capital". It has different meanings depending on whether the context is finance, law, geography, or grammar. Through language understanding, the semantic ranker can detect context and promote results that fit query intent.
 
 :::image type="content" source="media/semantic-search-overview/semantic-vector-representation.png" alt-text="Illustration of vector representation for context." border="true":::
 
 Semantic ranking is both resource and time intensive. In order to complete processing within the expected latency of a query operation, inputs to the semantic ranker are consolidated and reduced so that the reranking step can be completed as quickly as possible.
 
-There are two steps to semantic ranking: summarization and scoring. Outputs consist of rescored results, captions, and answers.
+There are three steps to semantic ranking:
+
+* Collect and summarize inputs
+* Score results using the semantic ranker
+* Output rescored results, captions, and answers
 
 ### How inputs are collected and summarized
 
 In semantic ranking, the query subsystem passes search results as an input to summarization and ranking models. Because the ranking models have input size constraints and are processing intensive, search results must be sized and structured (summarized) for efficient handling.
 
-1. Semantic ranker starts with a [BM25-ranked result](index-ranking-similarity.md) from a text query or an [RRF-ranked result](hybrid-search-ranking.md) from a hybrid query. Only text fields are used in the reranking exercise, and only the top 50 results progress to semantic ranking, even if results include more than 50. Typically, fields used in semantic ranking are informational and descriptive.
+1. Semantic ranker starts with a [BM25-ranked result](index-ranking-similarity.md) from a text query or an [RRF-ranked result](hybrid-search-ranking.md) from a vector or hybrid query. Only text fields are used in the reranking exercise, and only the top 50 results progress to semantic ranking, even if results include more than 50. Typically, fields used in semantic ranking are informational and descriptive.
 
 1. For each document in the search result, the summarization model accepts up to 2,000 tokens, where a token is approximately 10 characters. Inputs are assembled from the "title", "keyword", and "content" fields listed in the [semantic configuration](semantic-how-to-configure.md). 
 
@@ -78,31 +74,39 @@ In semantic ranking, the query subsystem passes search results as an input to su
 
    The maximum length of each generated summary string passed to the semantic ranker is 256 tokens. 
 
-### Outputs of semantic ranker
-
-From each summary string, the machine reading comprehension models find passages that are the most representative.
-
-Outputs are:
-
-* A [semantic caption](semantic-how-to-query-request.md) for the document. Each caption is available in a plain text version and a highlight version, and is frequently fewer than 200 words per document.
-
-* An optional [semantic answer](semantic-answers.md), assuming you specified the `answers` parameter, the query was posed as a question, and a passage is found in the long string that provides a likely answer to the question.
-
-Captions and answers are always verbatim text from your index. There's no generative AI model in this workflow that creates or composes new content.
-
-### How summaries are scored
+### How ranking is scored
 
 Scoring is done over the caption, and any other content from the summary string that fills out the 256 token length.
 
 1. Captions are evaluated for conceptual and semantic relevance, relative to the query provided.
 
 1. A **@search.rerankerScore** is assigned to each document based on the semantic relevance of the document for the given query. Scores range from 4 to 0 (high to low), where a higher score indicates higher relevance.
 
+   | Score | Meaning |
+   |-------|---------|
+   | 4.0   | The document is highly relevant and answers the question completely, though the passage might contain extra text unrelated to the question. |
+   | 3.0   | The document is relevant but lacks details that would make it complete.|
+   | 2.0   | The document is somewhat relevant; it answers the question either partially or only addresses some aspects of the question. |
+   | 1.0   | The document is related to the question, and it answers a small part of it. |
+   | 0.0   | The document is irrelevant. |
+
 1. Matches are listed in descending order by score and included in the query response payload. The payload includes answers, plain text and highlighted captions, and any fields that you marked as retrievable or specified in a select clause.
 
 > [!NOTE]
 > For any given query, the distributions of **@search.rerankerScore** can exhibit slight variations due to conditions at the infrastructure level. Ranking model updates have also been known to affect the distribution. For these reasons, if you're writing custom code for minimum thresholds, or [setting the threshold property](vector-search-how-to-query.md#set-thresholds-to-exclude-low-scoring-results-preview) for vector and hybrid queries, don't make the limits too granular.
 
+### Outputs of semantic ranker
+
+From each summary string, the machine reading comprehension models find passages that are the most representative.
+
+Outputs are:
+
+* A [semantic caption](semantic-how-to-query-request.md) for the document. Each caption is available in a plain text version and a highlight version, and is frequently fewer than 200 words per document.
+
+* An optional [semantic answer](semantic-answers.md), assuming you specified the `answers` parameter, the query was posed as a question, and a passage is found in the long string that provides a likely answer to the question.
+
+Captions and answers are always verbatim text from your index. There's no generative AI model in this workflow that creates or composes new content.
+
 ## Semantic capabilities and limitations
 
 Semantic ranker is a newer technology so it's important to set expectations about what it can and can't do. What it *can* do:
@@ -123,7 +127,7 @@ The following video provides an overview of the capabilities.
 
 ## Availability and pricing
 
-Semantic ranker is available on search services at the Basic and higher tiers, subject to [regional availability](https://azure.microsoft.com/global-infrastructure/services/?products=search).
+Semantic ranker is available on search services at the Basic and higher tiers, subject to [regional availability](search-region-support.md).
 
 When you enable semantic ranker, choose a pricing plan for the feature:
 
@@ -134,8 +138,20 @@ The [Azure AI Search pricing page](https://azure.microsoft.com/pricing/details/s
 
 Charges for semantic ranker are levied when query requests include `queryType=semantic` and the search string isn't empty (for example, `search=pet friendly hotels in New York`). If your search string is empty (`search=*`), you aren't charged, even if the queryType is set to semantic.
 
+## How to get started with semantic ranker
+
+1. [Check regional availability](search-region-support.md).
+
+1. [Sign in to Azure portal](https://portal.azure.com) to verify your search service is Basic or higher.
+
+1. [Enable semantic ranker and choose a pricing plan](semantic-how-to-enable-disable.md).
+
+1. [Configure semantic ranker in a search index](semantic-how-to-configure.md).
+
+1. [Set up queries to return semantic captions and highlights](semantic-how-to-query-request.md).
+
+1. [Optionally, return semantic answers](semantic-answers.md).
+
 ## See also
 
-* [Enable semantic ranker](semantic-how-to-enable-disable.md)
-* [Configure semantic ranker](semantic-how-to-query-request.md)
 * [Blog: Outperforming vector search with hybrid retrieval and ranking capabilities](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-cognitive-search-outperforming-vector-search-with-hybrid/ba-p/3929167)