@@ -49,7 +49,7 @@ RRF is used anytime there's more than one query execution. The following example
 
 Whenever results are ranked, **`@search.score`** property contains the value used to order the results. Scores are generated by ranking algorithms that vary for each method. Each algorithm has its own range and magnitude.
 
-The following chart identifies the scoring property returned on each match, algorithm, and range of scores for each relevance ranking algorithm. 
+The following chart identifies the scoring property returned on each match, algorithm, and range of scores for each relevance ranking algorithm. For more information and a diagram of the scoring workflow, see [Relevance in Azure AI Search](search-relevance-overview.md).
 
 | Search method | Parameter | Scoring algorithm | Range |
 |---------------|-----------|-------------------|-------|
@@ -141,38 +141,6 @@ By default, full text search is subject to a maximum limit of 1,000 matches (see
 
 For more information, see [How to work with search results](search-pagination-page-layout.md).
 
-## Diagram of a search scoring workflow
-
-The following diagram illustrates a hybrid query that invokes keyword and vector search, with [boosting through scoring profiles](index-add-scoring-profiles.md#how-search-scoring-works-in-azure-ai-search), and semantic ranking.
-
-:::image type="content" source="media/scoring-profiles/scoring-over-ranked-results.png" alt-text="Diagram of prefilters." border="true" lightbox="media/scoring-profiles/scoring-over-ranked-results.png":::
-
-A query that generates the previous workflow might look like this:
-
-```http
-POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2024-07-01
-Content-Type: application/json
-api-key: {{admin-api-key}}
-{
-   "queryType":"semantic",
-   "search":"hello world",
-   "searchFields":"field_a, field_b",
-   "vectorQueries": [
-       {
-           "kind":"vector",
-           "vector": [1.0, 2.0, 3.0],
-           "fields": "field_c, field_d"
-       },
-       {
-           "kind":"vector",
-           "vector": [4.0, 5.0, 6.0],
-           "fields": "field_d, field_e"
-       }
-   ],
-   "scoringProfile":"my_scoring_profile"
-}
-```
-
 ## See also
 
 + [Learn more about hybrid search](hybrid-search-overview.md)

@@ -10,42 +10,44 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 06/10/2025
+ms.date: 07/25/2025
 ---
 
 # Add scoring profiles to boost search scores
 
 Scoring profiles are used to boost the ranking of matching documents based on criteria. In this article, learn how to specify and assign a scoring profile that boosts a search score based on parameters that you provide. You can create scoring profiles based on:
 
-+ Weighted fields, where boosting is based on a match found in a specific string field. For example, if matches found in a "Subject" field should be more relevant than the same match found in a "Description" field.
++ Weighted string fields, where boosting is based on a match found in a designated field. For example, matches found in a "Subject" field are considered more relevant than the same match found in a "Description" field.
 
-+ Functions for numeric data, including dates, ranges, and geographic coordinates. There's also a Tags function that operates on a field providing an arbitrary collection of strings. You can choose this approach over weighted fields if you want to boost a score based on whether a match is found in a tags field.
++ Functions for numeric fields, including dates and geographic coordinates. Functions for numeric content support boosting on distance (applies to geographic coordinates), freshness (applies to datetime fields), range, and magnitude.
 
-You can add a scoring profile to an index by editing its JSON definition in the Azure portal or programmatically through APIs like [Create or Update Index REST](/rest/api/searchservice/indexes/create-or-update) or equivalent APIs in any Azure SDK.
++ Functions for string collections (tags). A tags function boosts a document's search score if any item in the collection is matched by the query.
+
+You can add a scoring profile to an index by editing its JSON definition in the Azure portal or programmatically through APIs like [Create or Update Index REST](/rest/api/searchservice/indexes/create-or-update) or equivalent index update APIs in any Azure SDK. There's no index rebuild requirements so you can add, modify, or delete a scoring profile with no effect on indexed documents.
 
 ## Prerequisites
 
-You can use any API version or SDK package for scoring profiles in keyword search. For vector and hybrid search, use 2024-05-01-preview and 2024-07-01 REST APIs or Azure SDK packages that provide feature parity. For integration between scoring profiles and semantic ranker, use 2025-05-01-preview and later.
++ A search index with text or numeric (nonvector) fields.
 
 ## Rules for scoring profiles
 
-You must have a new or existing search index with text or numeric fields. 
-
-You can use scoring profiles in [keyword search](search-lucene-query-architecture.md), [vector search](vector-search-overview.md), and [hybrid search](hybrid-search-overview.md). However, scoring profiles only apply to nonvector fields, so make sure your index has text or numeric fields that can be boosted or weighted. 
+You can use scoring profiles in [keyword search](search-lucene-query-architecture.md), [vector search](vector-search-overview.md), [hybrid search](hybrid-search-overview.md), and [semantic search (reranking)](semantic-search-overview.md). However, scoring profiles only apply to nonvector fields, so make sure your index has text or numeric fields that can be boosted or weighted. 
 
 You can have up to 100 scoring profiles within an index (see [service Limits](search-limits-quotas-capacity.md)), but you can only specify one profile at time in any given query.
 
-You can use [semantic ranker](semantic-how-to-query-request.md) with scoring profiles. When multiple ranking or relevance features are in play, semantic ranking is the last step. [How search scoring works](#how-search-scoring-works-in-azure-ai-search) provides an illustration.
+You can use [semantic ranker](semantic-how-to-query-request.md) with scoring profiles. Currently in preview, you can apply a [scoring profile after semantic ranking](semantic-how-to-enable-scoring-profiles.md). Otherwise, when multiple ranking or relevance features are in play, semantic ranking is the last step. [How search scoring works](search-relevance-overview.md#diagram-of-ranking-algorithms) provides an illustration.
+
+[Extra rules](#rules-for-using-functions) apply specifically to functions.
 
 > [!NOTE]
 > Unfamiliar with relevance concepts? Visit [Relevance and scoring in Azure AI Search](index-similarity-and-scoring.md) for background. You can also watch this [video segment on YouTube](https://www.youtube.com/embed/Y_X6USgvB1g?version=3&start=463&end=970) for scoring profiles over BM25-ranked results.
 >
 
 ## Scoring profile definition
 
-A scoring profile is named object defined in an index schema. A scoring profile is composed of weighted fields, functions, and parameters.
+A scoring profile is defined in an index schema. It consists of weighted fields, functions, and parameters.
 
-The following definition shows a simple profile named "geo". This example boosts results that have the search term in the hotelName field. It also uses the `distance` function to favor results that are within 10 kilometers of the current location. If someone searches on the term 'inn', and 'inn' happens to be part of the hotel name, documents that include hotels with 'inn' within a 10 KM radius of the current location will appear higher in the search results.  
+The following definition shows a simple profile named "geo". This example boosts results that have the search term in the hotelName field. It also uses the `distance` function to favor results that are within 10 kilometers of the current location. If someone searches on the term 'inn', and 'inn' happens to be part of the hotel name, documents that include hotels with 'inn' within a 10-kilometer radius of the current location appear higher in the search results.  
 
 ```json
 "scoringProfiles": [
@@ -83,83 +85,29 @@ POST /indexes/hotels/docs&api-version=2024-07-01
 }
 ```  
 
-This query searches on the term "inn" and passes in the current location. Notice that this query includes other parameters, such as scoringParameter. Query parameters, including "scoringParameter", are described in [Search Documents (REST API)](/rest/api/searchservice/documents/search-post).  
-
-See the [Extended example for vector and hybrid search](#extended-example-for-vector-and-hybrid-search) and [Extended example for keyword search](#extended-example-for-keyword-search) for more scenarios.
-
-## How search scoring works in Azure AI Search
-
-Scoring profiles supplement the default scoring algorithm by boosting the scores of matches that meet the profile's criteria. Scoring functions apply to:
-
-+ [Text (keyword) search](search-query-create.md)
-+ [Pure vector queries](vector-search-how-to-query.md)
-+ [Hybrid queries](hybrid-search-how-to-query.md), with text and vector subqueries execute in parallel
+Query parameters, including `scoringParameters`, are described in [Search Documents (REST API)](/rest/api/searchservice/documents/search-post).  
 
-For standalone text queries, scoring profiles identify the maximum 1,000 matches in a [BM25-ranked search](index-similarity-and-scoring.md), and the top 50 are returned in results.
-
-For pure vectors, the query is vector-only, but if the [*k*-matching documents](vector-search-ranking.md) include nonvector fields with human-readable content, a scoring profile can be applied. The scoring profile revises the result set by boosting documents that match criteria in the profile.
-
-For text queries in a hybrid query, scoring profiles identify the maximum 1,000 matches in a BM25-ranked search. However, once those 1,000 results are identified, they're restored to their original BM25 order so that they can be rescored alongside vectors results in the final [Reciprocal Ranking Function (RRF)](hybrid-search-ranking.md) ordering, where the scoring profile (identified as "final document boosting adjustment" in the illustration) is applied to the merged results, along with [vector weighting](vector-search-how-to-query.md#vector-weighting), and [semantic ranking](semantic-search-overview.md) as the last step.
-
-:::image type="content" source="media/scoring-profiles/scoring-over-ranked-results.png" alt-text="Diagram showing which fields have a scoring profile and when ranking occurs.":::
+For more scenarios, see the examples for [freshness and distance](#example-boosting-by-freshness-or-distance) and [weighted text and functions](#example-boosting-by-weighted-text-and-functions) in this article.
 
 ## Add a scoring profile to a search index
 
-1. Start with an [index definition](/rest/api/searchservice/indexes/create). You can add and update scoring profiles on an existing index without having to rebuild it. Use an [Create or Update Index](/rest/api/searchservice/indexes/create-or-update) request to post a revision.
+1. Start with an [index definition](/rest/api/searchservice/indexes/create). You can add and update scoring profiles on an existing index without having to rebuild it. Use a [Create or Update Index](/rest/api/searchservice/indexes/create-or-update) request to post a revision.
 
 1. Paste in the [template](#template) provided in this article.  
 
-1. Provide a name that adheres to [Azure AI Search naming conventions](/rest/api/searchservice/naming-rules).
+1. Provide a name that adheres to [naming conventions](/rest/api/searchservice/naming-rules).
 
 1. Specify boosting criteria. A single profile can contain [text weighted fields](#use-text-weighted-fields), [functions](#use-functions), or both. 
 
-You should work iteratively, using a data set that will help you prove or disprove the efficacy of a given profile.
+You should work iteratively, using a data set that helps you prove or disprove the efficacy of a given profile.
 
-Scoring profiles can be defined in Azure portal as shown in the following screenshot, or programmatically through [REST APIs](/rest/api/searchservice/indexes/create-or-update) or in Azure SDKs, such as the [ScoringProfile](/dotnet/api/azure.search.documents.indexes.models.scoringprofile) class in the Azure SDK for .NET.
+Scoring profiles can be defined in the Azure portal as shown in the following screenshot, or programmatically through [REST APIs](/rest/api/searchservice/indexes/create-or-update) or in Azure SDKs, such as the ScoringProfile class in [.NET](/dotnet/api/azure.search.documents.indexes.models.scoringprofile) or [Python](/python/api/azure-search-documents/azure.search.documents.indexes.models.scoringprofile) client libraries.
 
 :::image type="content" source="media/scoring-profiles/portal-add-scoring-profile-small.png" alt-text="Screenshot showing the Add scoring profile option in the Azure portal." lightbox="media/scoring-profiles/portal-add-scoring-profile.png" border="true":::
 
-## Use text-weighted fields
-
-Use text-weighted fields when field context is important and queries include `searchable` string fields. For example, if a query includes the term "airport", you might want "airport" in the Description field to have more weight than in the HotelName. 
-
-Weighted fields are name-value pairs composed of a `searchable` field and a positive number that is used as a multiplier. If the original field score of HotelName is 3, the boosted score for that field becomes 6, contributing to a higher overall score for the parent document itself.
-
-```json
-"scoringProfiles": [  
-    {  
-      "name": "boostSearchTerms",  
-      "text": {  
-        "weights": {  
-          "HotelName": 2,  
-          "Description": 5 
-        }  
-      }  
-    }
-]
-```
-
-## Use functions
-
-Use functions when simple relative weights are insufficient or don't apply, as is the case of distance and freshness, which are calculations over numeric data. You can specify multiple functions per scoring profile. For more information about the EDM data types used in Azure AI Search, see [Supported data types](/rest/api/searchservice/supported-data-types).
-
-| Function | Description | Use cases |
-|-|-|
-| distance  | Boost by proximity or geographic location. This function can only be used with `Edm.GeographyPoint` fields. | Use for "find near me" scenarios. |
-| freshness | Boost by values in a datetime field (`Edm.DateTimeOffset`). [Set boostingDuration](#set-boostingduration-for-freshness-function) to specify a value representing a timespan over which boosting occurs. | Use when you want to boost by newer or older dates. Rank items like calendar events with future dates such that items closer to the present can be ranked higher than items further in the future. One end of the range is fixed to the current time. To boost a range of times in the past, use a positive boostingDuration. To boost a range of times in the future, use a negative boostingDuration. |
-| magnitude | Alter rankings based on the range of values for a numeric field. The value must be an integer or floating-point number. For star ratings of 1 through 4, this would be 1. For margins over 50%, this would be 50. This function can only be used with `Edm.Double` and `Edm.Int` fields. For the magnitude function, you can reverse the range, high to low, if you want the inverse pattern (for example, to boost lower-priced items more than higher-priced items). Given a range of prices from $100 to $1, you would set `boostingRangeStart` at 100 and `boostingRangeEnd` at 1 to boost the lower-priced items. | Use when you want to boost by profit margin, ratings, clickthrough counts, number of downloads, highest price, lowest price, or a count of downloads. When two items are relevant, the item with the higher rating will be displayed first. |
-| tag  | Boost by tags that are common to both search documents and query strings. Tags are provided in a `tagsParameter`. This function can only be used with search fields of type `Edm.String` and `Collection(Edm.String)`. | Use when you have tag fields. If a given tag within the list is itself a comma-delimited list, you can [use a text normalizer](search-normalizers.md) on the field to strip out the commas at query time (map the comma character to a space). This approach will "flatten" the list so that all terms are a single, long string of comma-delimited terms. | 
-
-### Rules for using functions
-
-+ Functions can only be applied to fields that are attributed as `filterable`.
-+ Function type ("freshness", "magnitude", "distance", "tag") must be lower case.
-+ Functions can't include null or empty values.
-+ Functions can only have a single field per function definition. To use magnitude twice in the same profile, provide two definitions magnitude, one for each field.
-
-## Template
+### Template
 
- This section shows the syntax and template for scoring profiles. For a description of properties, see the [REST API reference](/rest/api/searchservice/indexes/create?view=rest-searchservice-2024-07-01&preserve-view=true#scoringfunctionaggregation).
+ This section shows the syntax and template for scoring profiles. For a description of properties, see the [REST API reference](/rest/api/searchservice/indexes/create#scoringfunctionaggregation).
 
 ```json
 "scoringProfiles": [  
@@ -174,7 +122,7 @@ Use functions when simple relative weights are insufficient or don't apply, as i
     "functions": (optional) [  
       {   
         "type": "magnitude | freshness | distance | tag",   
-        "boost": # (positive number used as multiplier for raw score != 1),   
+        "boost": # (positive or negative number used as multiplier for raw score != 1),   
         "fieldName": "(...)",   
         "interpolation": "constant | linear (default) | quadratic | logarithmic",   
 
@@ -210,22 +158,68 @@ Use functions when simple relative weights are insufficient or don't apply, as i
 "defaultScoringProfile": (optional) "...", 
 ```
 
-## Set interpolations
+## Use text-weighted fields
 
-Interpolations set the shape of the slope used for scoring. Because scoring is high to low, the slope is always decreasing, but the interpolation determines the curve of the downward slope. The following interpolations can be used:  
+Use text-weighted fields when field context is important and queries include `searchable` string fields. For example, if a query includes the term "airport", you might want "airport" in the HotelName field rather than the Description field. 
+
+Weighted fields are name-value pairs composed of a `searchable` field and a positive number that is used as a multiplier. If the original field score of HotelName is 3, the boosted score for that field becomes 6, contributing to a higher overall score for the parent document itself.
+
+```json
+"scoringProfiles": [  
+    {  
+      "name": "boostSearchTerms",  
+      "text": {  
+        "weights": {  
+          "HotelName": 2,  
+          "Description": 5 
+        }  
+      }  
+    }
+]
+```
+
+## Use functions
+
+Use functions when simple relative weights are insufficient or don't apply, as is the case of distance and freshness, which are calculations over numeric data. You can specify multiple functions per scoring profile. For more information about the EDM data types used in Azure AI Search, see [Supported data types](/rest/api/searchservice/supported-data-types).
+
+| Function | Description | Use cases |
+|-|-|
+| distance  | Boost by proximity or geographic location. This function can only be used with `Edm.GeographyPoint` fields. | Use for "find near me" scenarios. |
+| freshness | Boost by values in a datetime field (`Edm.DateTimeOffset`). [Set boostingDuration](#set-boostingduration-for-freshness-function) to specify a value representing a timespan over which boosting occurs. | Use when you want to boost by newer or older dates. Rank items like calendar events with future dates such that items closer to the present can be ranked higher than items further in the future. One end of the range is fixed to the current time. To boost a range of times in the past, use a positive boostingDuration. To boost a range of times in the future, use a negative boostingDuration. |
+| magnitude | Alter rankings based on the range of values for a numeric field. The value must be an integer or floating-point number. For star ratings of 1 through 4, this would be 1. For margins over 50%, this would be 50. This function can only be used with `Edm.Double` and `Edm.Int` fields. For the magnitude function, you can reverse the range, high to low, if you want the inverse pattern (for example, to boost lower-priced items more than higher-priced items). Given a range of prices from $100 to $1, you would set `boostingRangeStart` at 100 and `boostingRangeEnd` at 1 to boost the lower-priced items. | Use when you want to boost by profit margin, ratings, clickthrough counts, number of downloads, highest price, lowest price, or a count of downloads. When two items are relevant, the item with the higher rating is displayed first. |
+| tag  | Boost by tags that are common to both search documents and query strings. Tags are provided in a `tagsParameter`. This function can only be used with search fields of type `Edm.String` and `Collection(Edm.String)`. | Use when you have tag fields. If a given tag within the list is itself a comma-delimited list, you can [use a text normalizer](search-normalizers.md) on the field to strip out the commas at query time (map the comma character to a space). This approach "flattens" the list so that all terms are a single, long string of comma-delimited terms. | 
+
+Magnitude is the computed distance between a field's value (such as a date or location) and a reference point (such as "now" or a target location). It's the input to the scoring function and determines how much boost is applied. 
+
+Freshness and distance scoring are special cases of magnitude-based scoring, where the magnitude is automatically computed from a datetime or geographic field. For intuitive boosting that promotes newer or closer values over older or farther values, use a negative boost value (see the [example](#example-boosting-by-freshness-or-distance) for more details).
+
+### Rules for using functions
+
++ Functions can only be applied to fields that are attributed as `filterable`.
++ Function type ("freshness", "magnitude", "distance", "tag") must be lower case.
++ Functions can't include null or empty values.
++ Functions can only have a single field per function definition. To use magnitude twice in the same profile, provide two definitions magnitude, one for each field.
+
+### Set interpolations
+
+Interpolations set the shape of the slope used for boosting freshness and distance. 
+
+When the boost value is positive, scoring is high to low, and the slope is always decreasing. With negative boosts, the slope is increasing (newer documents get higher scores). The interpolation values determines the curve of the upward or downward slope and how aggressively the boost score changes in response to date or distance changes. The following interpolations can be used:  
 
 | Interpolation | Description |  
 |-|-|  
-|`linear`|For items that are within the max and min range, boosting is applied in a constantly decreasing amount. Linear is the default interpolation for a scoring profile.|  
-|`constant`|For items that are within the start and ending range, a constant boost is applied to the rank results.|  
-|`quadratic`|In comparison to a linear interpolation that has a constantly decreasing boost, Quadratic initially decreases at smaller pace and then as it approaches the end range, it decreases at a much higher interval. This interpolation option isn't allowed in tag scoring functions.|  
-|`logarithmic`|In comparison to a linear interpolation that has a constantly decreasing boost, logarithmic initially decreases at higher pace and then as it approaches the end range, it decreases at a much smaller interval. This interpolation option isn't allowed in tag scoring functions.|  
+|`linear`|For items that are within the max and min range, boosting is applied in a constantly decreasing amount. A negative boost penalizes older documents proportionally. Good for gradual decay in relevance. Linear is the default interpolation for a scoring profile.|  
+|`constant`|For items that are within the start and ending range, a constant boost is applied to the rank results. For freshness and distance, applies the same negative boost to all documents within the range. Use this when you want a flat penalty regardless of age.|  
+|`quadratic`|Quadratic initially decreases at smaller pace and then as it approaches the end range, it decreases at a much higher interval. For negative boosting, it penalizes older documents increasingly more as they age. Use this when you want to strongly favor the most recent documents and sharply demote older ones. This interpolation option isn't allowed in the tag scoring function.|  
+|`logarithmic` |Logarithmic initially decreases at higher pace and then as it approaches the end range, it decreases at a much smaller interval. For negative boosting, it penalizes older documents more sharply at first, then tapers off. Ideal when you want strong preference for very recent content but less sensitivity as documents age. This interpolation option isn't allowed in the tag scoring function.|  
 
- ![Constant, linear, quadratic, log10 lines on graph](media/scoring-profiles/azuresearch_scorefunctioninterpolationgrapht.png "AzureSearch_ScoreFunctionInterpolationGrapht")  
+<!--  ![Constant, linear, quadratic, log10 lines on graph](media/scoring-profiles/azuresearch_scorefunctioninterpolationgrapht.png "AzureSearch_ScoreFunctionInterpolationGrapht") -->
+  
+:::image type="content" source="media/scoring-profiles/interpolation-graph.png" alt-text="Diagram of slope shapes for constant, linear, logarithmic, and quadratic interpolations over a 365 day range":::
 
-## Set boostingDuration for freshness function
+### Set boostingDuration for freshness function
 
-`boostingDuration` is an attribute of the `freshness` function. You use it to set an expiration period after which boosting will stop for a particular document. For example, to boost a product line or brand for a 10-day promotional period, you would specify the 10-day period as "P10D" for those documents.  
+`boostingDuration` is an attribute of the `freshness` function. You use it to set an expiration period after which boosting stops for a particular document. For example, to boost a product line or brand for a 10-day promotional period, you would specify the 10-day period as "P10D" for those documents.  
 
 `boostingDuration` must be formatted as an XSD "dayTimeDuration" value (a restricted subset of an ISO 8601 duration value). The pattern for this is: "P[nD][T[nH][nM][nS]]".  
 
@@ -236,19 +230,61 @@ The following table provides several examples.
 |1 day|"P1D"|  
 |2 days and 12 hours|"P2DT12H"|  
 |15 minutes|"PT15M"|  
-|30 days, 5 hours, 10 minutes, and 6.334 seconds|"P30DT5H10M6.334S"|  
+|30 days, 5 hours, 10 minutes, and 6.334 seconds|"P30DT5H10M6.334S"|
+|1 year | "365D" |
 
 For more examples, see [XML Schema: Datatypes (W3.org web site)](https://www.w3.org/TR/xmlschema11-2/#dayTimeDuration).
 
-## Extended example for vector and hybrid search
+## Example: boosting by freshness or distance
+
+In Azure AI Search, freshness scoring converts date and values into a numeric magnitude—a single number representing how far a document's date is from the current time. The older the date, the larger the magnitude. This leads to a counter-intuitive behavior: more recent documents have smaller magnitudes, which means that positive boosting factors favor older documents unless you explicitly invert the boost direction.
 
-See this [blog post](https://farzzy.hashnode.dev/enhance-azure-ai-search-document-boosting) and [notebook](https://github.com/farzad528/azure-ai-search-python-playground/blob/main/azure-ai-search-document-boosting.ipynb) for a demonstration of using scoring profiles and document boosting in vector and generative AI scenarios.
+This same logic applies to distance boosting, where farther locations yield larger magnitudes.
 
-## Extended example for keyword search
+To boost by freshness or distance, use negative boosting values to prioritize newer dates or closer locations. Inverting the boost direction through a negative boosting factor penalizes larger magnitudes (older dates), effectively boosting more recent ones. For example, assume a boosting function like `b * (1 - x)` (where `x` is the normalized magnitude from 0 to 1) that gives higher scores to smaller magnitudes (that is, newer dates).
 
-The following example shows the schema of an index with two scoring profiles (`boostGenre`, `newAndHighlyRated`). Any query against this index that includes either profile as a query parameter will use the profile to score the result set. 
+The shape of the boost curve (constant, linear, logarithmic, quadratic) affects how aggressively scores change across the range. With a negative factor, the curve’s behavior flips—for example, a quadratic curve tapers off more slowly for older dates, while a logarithmic curve shifts more sharply at the far end.
 
-The `boostGenre` profile uses weighted text fields, boosting matches found in albumTitle, genre, and artistName fields. The fields are boosted 1.5, 5, and 2 respectively. Why is genre boosted so much higher than the others? If search is conducted over data that is somewhat homogeneous (as is the case with 'genre' in the musicstoreindex), you might need a larger variance in the relative weights. For example, in the musicstoreindex, 'rock' appears as both a genre and in identically phrased genre descriptions. If you want genre to outweigh genre description, the genre field will need a much higher relative weight.
+Here's an example scoring profile that demonstrates how to address counter-intuitive freshness scoring using negative boosting and explains how magnitude works in this context.
+
+```json
+
+"scoringProfiles": [
+  {
+    "name": "freshnessBoost",
+    "text": {
+      "weights": {
+        "content": 1.0
+      }
+    },
+    "functions": [
+      {
+        "type": "freshness",
+        "fieldName": "lastUpdated",
+        "boost": -2.0,
+        "interpolation": "quadratic",
+        "parameters": {
+          "boostingDuration": "365D"
+        }
+      }
+    ]
+  }
+]
+```
+
++ `"fieldName": "lastUpdated"` is the datetime field used to calculate freshness.
++ `"boost": -2.0` is a negative boosting factor, which inverts the default behavior. Since older dates have larger magnitudes, this penalizes them and boosts newer documents.
++ `"interpolation": "quadratic"` means the boost effect is stronger for documents closer to the current date and tapers off more sharply for older ones.
++ `"boostingDuration": "365D"` defines the time window over which freshness is evaluated.
+
+## Example: boosting by weighted text and functions
+
+> [!TIP]
+> See this [blog post](https://farzzy.hashnode.dev/enhance-azure-ai-search-document-boosting) and [notebook](https://github.com/farzad528/azure-ai-search-python-playground/blob/main/azure-ai-search-document-boosting.ipynb) for a demonstration of using scoring profiles and document boosting in vector and generative AI scenarios.
+
+The following example shows the schema of an index with two scoring profiles (`boostGenre`, `newAndHighlyRated`). Any query against this index that includes either profile as a query parameter uses the profile to score the result set. 
+
+The `boostGenre` profile uses weighted text fields, boosting matches found in albumTitle, genre, and artistName fields. The fields are boosted 1.5, 5, and 2 respectively. Why is genre boosted so much higher than the others? If search is conducted over data that is somewhat homogeneous (as is the case with 'genre' in the musicstoreindex), you might need a larger variance in the relative weights. For example, in the musicstoreindex, 'rock' appears as both a genre and in identically phrased genre descriptions. If you want genre to outweigh genre description, the genre field needs a much higher relative weight.
 
 ```json
 {  
@@ -285,7 +321,7 @@ The `boostGenre` profile uses weighted text fields, boosting matches found in al
         {  
           "type": "freshness",  
           "fieldName": "lastUpdated",  
-          "boost": 10,  
+          "boost": -10,  
           "interpolation": "quadratic",  
           "freshness": {  
             "boostingDuration": "P365D"  
@@ -314,11 +350,3 @@ The `boostGenre` profile uses weighted text fields, boosting matches found in al
   ]   
 }  
 ```  
-
-## See also
-
-+ [Relevance and scoring in Azure AI Search](index-similarity-and-scoring.md)
-+ [REST API Reference](/rest/api/searchservice/)
-+ [Create Index API](/rest/api/searchservice/indexes/create)
-+ [Azure AI Search .NET SDK](/dotnet/api/overview/azure/search?)
-+ [What Are Scoring Profiles?](https://social.technet.microsoft.com/wiki/contents/articles/26706.azure-search-what-are-scoring-profiles.aspx)

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/18/2025
+ms.date: 07/25/2025
 ---
 
 # How to model relational SQL data for import and indexing in Azure AI Search

@@ -1,7 +1,7 @@
 ---
 title: Monitoring data reference for Azure AI Search
 description: This article contains important reference material you need when you monitor Azure AI Search.
-ms.date: 01/27/2025
+ms.date: 07/25/2025
 ms.custom: horz-monitor
 ms.topic: reference
 author: HeidiSteen

@@ -1,9 +1,9 @@
 ---
 title: Monitor Azure AI Search
 description: Start here to learn how to monitor Azure AI Search.
-ms.date: 01/27/2025
+ms.date: 07/25/2025
 ms.custom: horz-monitor
-ms.topic: conceptual
+ms.topic: concept-article
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
@@ -93,6 +93,7 @@ AzureDiagnostics
 [!INCLUDE [horz-monitor-alerts](~/reusable-content/ce-skilling/azure/includes/azure-monitor/horizontals/horz-monitor-alerts.md)]
 
 ### Azure AI Search alert rules
+
 The following table lists common and recommended alert rules for Azure AI Search. On a search service, throttling or query latency that exceeds a given threshold are the most commonly used alerts, but you might also want to be notified if a search service is deleted.
 
 | Alert type | Condition | Description  |

@@ -130,7 +130,7 @@ Proximity searches are used to find terms that are near each other in a document
 
 Term boosting refers to ranking a document higher if it contains the boosted term, relative to documents that don't contain the term. This differs from scoring profiles in that scoring profiles boost certain fields, rather than specific terms.  
 
-The following example helps illustrate the differences. Suppose that there's a scoring profile that boosts matches in a certain field, say *genre* in the  [musicstoreindex example](index-add-scoring-profiles.md#extended-example-for-keyword-search). Term boosting could be used to further boost certain search terms higher than others. For example, `rock^2 electronic` boosts documents that contain the search terms in the genre field higher than other searchable fields in the index. Further, documents that contain the search term *rock* are ranked higher than the other search term *electronic* as a result of the term boost value (2).  
+The following example helps illustrate the differences. Suppose that there's a scoring profile that boosts matches in a certain field, say *genre* in the  [musicstoreindex example](index-add-scoring-profiles.md#example-boosting-by-weighted-text-and-functions). Term boosting could be used to further boost certain search terms higher than others. For example, `rock^2 electronic` boosts documents that contain the search terms in the genre field higher than other searchable fields in the index. Further, documents that contain the search term *rock* are ranked higher than the other search term *electronic* as a result of the term boost value (2).  
 
  To boost a term, use the caret, `^`, symbol with a boost factor (a number) at the end of the term you're searching. You can also boost phrases. The higher the boost factor, the more relevant the term is relative to other search terms. By default, the boost factor is 1. Although the boost factor must be positive, it can be less than 1 (for example, 0.20).  
 

@@ -196,7 +196,7 @@ PUT https://{{search-url}}/agents/{{agent-name}}?api-version=2025-05-01-preview
     }
     ```
 
-+ `defaultRerankerThreshold` is the minimum semantic reranker score that's acceptable for inclusion in a response. [Reranker scores](semantic-search-overview.md#how-ranking-is-scored) range from 1 to 4. Plan on revising this value based on testing and what works for your content.
++ `defaultRerankerThreshold` is the minimum semantic reranker score that's acceptable for inclusion in a response. [Reranker scores](semantic-search-overview.md#how-results-are-scored) range from 1 to 4. Plan on revising this value based on testing and what works for your content.
 
 + `defaultIncludeReferenceSourceData` is a boolean that determines whether the reference portion of the response includes source data. We recommend starting with this value set to true if you want to shape your own response using output from the search engine. Otherwise, if you want to use the output in the response `content` string, you can set it to false.
 

@@ -103,7 +103,7 @@ POST https://{{search-url}}/agents/{{agent-name}}/retrieve?api-version=2025-05-0
 
   + `rerankerThreshold` and `maxDocsForReranker` are also initially set in the knowledge agent definition as defaults. You can override them in the retrieve action to configure [semantic reranker](semantic-how-to-configure.md), setting minimum thresholds and the maximum number of inputs sent to the reranker.
 
-    `rerankerThreshold` is the minimum semantic reranker score that's acceptable for inclusion in a response. [Reranker scores](semantic-search-overview.md#how-ranking-is-scored) range from 1 to 4. Plan on revising this value based on testing and what works for your content.
+    `rerankerThreshold` is the minimum semantic reranker score that's acceptable for inclusion in a response. [Reranker scores](semantic-search-overview.md#how-results-are-scored) range from 1 to 4. Plan on revising this value based on testing and what works for your content.
 
     `maxDocsForReranker` dictates the maximum number of documents to consider for the final response string. Semantic reranker accepts 50 documents. If the maximum is 200, four more subqueries are added to the query plan to ensure all 200 documents are semantically ranked. for semantic ranking. If the number isn't evenly divisible by 50, the query plan rounds up to nearest whole number. 
 

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 01/15/2025
+ms.date: 07/25/2025
 ms.custom:
   - ignite-2023
   - sfi-image-nochange

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/16/2025
+ms.date: 07/25/2025
 ---
 
 # Index JSON blobs and files in Azure AI Search

@@ -10,8 +10,8 @@ ms.service: azure-ai-search
 ms.custom:
   - devx-track-dotnet
   - ignite-2023
-ms.topic: conceptual
-ms.date: 01/17/2025
+ms.topic: concept-article
+ms.date: 07/25/2025
 ---
 
 # Monitor indexer status and results in Azure AI Search

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 01/28/2025
+ms.date: 07/25/2025
 ---
 
 # Configure diagnostic logging for Azure AI Search

@@ -9,8 +9,8 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 01/27/2025
+ms.topic: concept-article
+ms.date: 07/25/2025
 ---
 
 # Monitor query requests in Azure AI Search

@@ -0,0 +1,131 @@
+---
+title: Relevance
+titleSuffix: Azure AI Search
+description: Describes how the scoring and ranking algorithms work in Azure AI Search and how to use them together.
+
+manager: nitinme
+author: HeidiSteen
+ms.author: heidist
+ms.service: azure-ai-search
+ms.topic: concept-article
+ms.date: 07/23/2025
+---
+
+# Relevance in Azure AI Search
+
+In a query operation, the relevance of any given result is measured by a ranking algorithm that determines the strength of a match based on how closely the result aligns with the query’s content or characteristics. An algorithm assigns a score, and results are rank ordered by that score, with the most relevant matches returned in the response. 
+
+Ranking occurs whenever the query request includes full text or vector queries. It doesn't occur if the query invokes strict pattern matching, such as a filter-only query or a specialized query form like autocomplete, suggestions, geospatial search, fuzzy search, or regular expression search. A uniform search score of 1.0 indicates the absence of a ranking algorithm.
+
+The query engine in Azure AI Search supports a multi-level approach to ranking search results, where there's a built-in ranking modality for each query type, plus extra ranking capabilities for extended relevance tuning.
+
+## Levels of ranking
+
+This section describes the levels of scoring operations. For an illustration of how they work together, see the [diagram](#diagram-of-ranking-algorithms) in this article. A [comparison of all search score types and ranges](#types-of-search-scores) is also provided in this article.
+
+| Level | Description |
+|-------|-------------|
+| Level&nbsp;1&nbsp;(L1) | Initial search score (`@search.score`). <br>For text queries matching on tokenized strings, results are always initially ranked using the [BM25 ranking algorithm](index-similarity-and-scoring.md). <br>For vector queries, results are ranked using either [Hierarchical Navigable Small World (HNSW) or exhaustive K-nearest neighbor (KNN)](vector-search-ranking.md). Image search or multimodal searches are based on vector queries and scored using the L1 vector ranking algorithms. |
+| Fused&nbsp;L1 | Scoring from multiple queries using the [Reciprocal Ranking Fusion (RRF) algorithm](hybrid-search-ranking.md). RRF is used for hybrid queries that include text and vector components. RRF is also used when multiple vector queries execute in parallel. A search score from RRF is reflected in `@search.score` [over a different range](#types-of-search-scores).|
+| Level&nbsp;2&nbsp;(L2) | [Semantic ranking score (`@search.reRankerScore`)](semantic-search-overview.md) applies machine reading comprehension to the textual content retrieved by L1 ranking, rescoring the L1 results to better match the semantic intent of the query. L2 reranks L1 results because doing so saves time and money; it would be prohibitive to use semantic ranking as an L1 ranking system. Semantic ranking is a premium feature that bills for usage of the semantic ranking models. It's optional for text queries and vector queries that contain text, but required for [agentic retrieval (preview)](search-agentic-retrieval-concept.md). Although agentic retrieval sends multiple queries to the query engine, the ranking algorithm for agentic retrieval is the semantic ranker. |
+
+## Custom boosting logic using scoring profiles
+
+[Scoring profiles](index-add-scoring-profiles.md) are an optional feature for boosting scores based on extra user-defined criteria. Criteria can include weighted fields, or functions that boost by freshness, proximity, magnitude, or range. There's no extra charge for using a scoring profile. To use a scoring profile, you define it in an index and then specify it on a query. 
+
+Scoring logic applies to text and numeric nonvector content. You can use scoring profiles with:
+
++ [Text (keyword) search](search-query-create.md)
++ [Pure vector queries](vector-search-how-to-query.md)
++ [Hybrid queries](hybrid-search-how-to-query.md), with text and vector subqueries execute in parallel
++ [Semantically ranked queries](semantic-how-to-query-request.md)
+
+For standalone text queries, scoring profiles identify the top 1,000 matches in a [BM25-ranked search](index-similarity-and-scoring.md), with the top 50 matches returned in the response.
+
+For pure vectors, the query is vector-only, but if the [*k*-matching documents](vector-search-ranking.md) include nonvector fields with human-readable content, a scoring profile is applied to nonvector fields in `k` documents. 
+
+For the text component of a hybrid query, scoring profiles identify the top 1,000 matches in a BM25-ranked search. However, once those 1,000 results are identified, they're restored to their original BM25 order so that they can be rescored alongside vectors results in the final [Reciprocal Ranking Function (RRF)](hybrid-search-ranking.md) ordering, where the scoring profile (identified as "final document boosting adjustment" in the illustration) is applied to the merged results, along with [vector weighting](vector-search-how-to-query.md#vector-weighting), and [semantic ranking](semantic-search-overview.md) as the last step.
+
+For semantically ranked queries (not shown in the diagram), assuming you use the latest preview REST API or a preview Azure SDK package, scoring profiles can be applied over an L2 ranked result set, generating a new `@search.rerankerBoostedScore` that determines the final ranking.
+
+## Types of search scores
+
+Scored results are indicated for each match in the query response. This table lists all of the search scores with an associated range. Range varies by algorithm.
+
+| Score | Range | Algorithm|
+|-------|-------|-------------|
+| `@search.score` | 0 through unlimited | [BM25 ranking algorithm](index-similarity-and-scoring.md#scores-in-a-text-results) for text search |
+| `@search.score` | 0.333 - 1.00 | [HNSW or exhaustive KNN algorithm](vector-search-ranking.md#scores-in-a-vector-search-results) for vector search |
+| `@search.score` | 0 through an upper limit determined by the number of queries | [RRF algorithm](hybrid-search-ranking.md#scores-in-a-hybrid-search-results) |
+| `@search.rerankerScore` | 0.00 - 4.00 | [Semantic ranking algorithm](semantic-search-overview.md#how-results-are-scored) for L2 ranking |
+| `@search.rerankerScoreBoosted` | 0 through unlimited  | [Semantic ranking with scoring profile boosting](semantic-how-to-enable-scoring-profiles.md) (scores can be significantly higher than 4) |
+
+## Diagram of ranking algorithms
+
+The following diagram illustrates how the ranking algorithms work together.
+
+:::image type="content" source="media/scoring-profiles/scoring-over-ranked-results.png" alt-text="Diagram showing which fields have a scoring profile and when ranking occurs.":::
+
+> [!NOTE]
+> This workflow diagram currently omits `@search.rerankerScoreBoosted` and a step for semantic ranking with boosting from a scoring profile. If you use semantic ranking with scoring profile, the scoring profile is applied after L2 ranking, and the final score is based on `@search.rerankerScoreBoosted`.
+
+## Example query inclusive of all ranking algorithms
+
+A query that generates the previous workflow might look like the following example. This hybrid semantic query is scored using RRF (based on L1 scores for text and vectors), and semantic ranking.
+
+```http
+POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2025-05-01-preview
+
+{
+  "search": "cloud formation over water",
+  "count": true,
+  "vectorQueries": [
+    {
+      "kind": "text",
+      "text": "cloud formation over water",
+      "fields": "text_vector,image_vector"
+    }
+  ],
+  "queryType": "semantic",
+  "semanticConfiguration": "my-semantic-configuration",
+  "select": "title,chunk",
+  "top": 5
+}
+```
+
+A response for the above query includes the original RRF `@search.core` and the `@search.rerankerScore`.
+
+```json
+  "value": [
+    {
+      "@search.score": 0.03177805617451668,
+      "@search.rerankerScore": 2.6919238567352295,
+      "chunk": "A\nT\n\nM\nO\n\nS\nP\n\nH\nE\n\nR\nE\n\nE\nA\n\nR\nT\n\nH\n\n32\n\nFraming an Iceberg\nSouth Atlantic Ocean\n\nIn June 2016, the Suomi NPP satellite captured this image of various cloud formations in the South Atlantic Ocean. Note how low \n\nstratus clouds framed a hole over iceberg A-56 as it drifted across the sea. \n\nThe exact reason for the hole in the clouds is somewhat of a mystery. It could have formed by chance, although imagery from the \n\ndays before and after this date suggest something else was at work. It could be that the relatively unobstructed path of the clouds \n\nover the ocean surface was interrupted by thermal instability created by the iceberg. In other words, if an obstacle is big enough,  \n\nit can divert the low-level atmospheric flow of air around it, a phenomenon often caused by islands.",
+      "title": "page-39.pdf",
+    },
+    {
+      "@search.score": 0.030621785670518875,
+      "@search.rerankerScore": 2.557225465774536,
+      "chunk": "A\nT\n\nM\nO\n\nS\nP\n\nH\nE\n\nR\nE\n\nE\nA\n\nR\nT\n\nH\n\n24\n\nMaking Tracks\nPacific Ocean\n\nShips steaming across the Pacific Ocean left this cluster of bright cloud trails lingering in the atmosphere in February 2012. The \n\nnarrow clouds, known as ship tracks, form when water vapor condenses around tiny particles of pollution from ship exhaust. The \n\ncrisscrossing clouds off the coast of California stretched for many hundreds of kilometers from end to end. The narrow ends of the \n\nclouds are youngest, while the broader, wavier ends are older.\n\nSome of the pollution particles generated by ships (especially sulfates) are soluble in water and can serve as the seeds around which \n\ncloud droplets form. Clouds infused with ship exhaust have more and smaller droplets than unpolluted clouds. As a result, light \n\nhitting the ship tracks scatters in many directions, often making them appear brighter than other types of marine clouds, which are \n\nusually seeded by larger, naturally occurring particles like sea salt.",
+      "title": "page-31.pdf",
+    },
+    {
+      "@search.score": 0.013698630034923553,
+      "@search.rerankerScore": 2.515575408935547,
+      "chunk": "A\nT\n\nM\nO\n\nS\nP\n\nH\nE\n\nR\nE\n\nE\nA\n\nR\nT\n\nH\n\n16\n\nRiding the Waves\nMauritania\n\nYou cannot see it directly, but air masses from Africa and the Atlantic Ocean are colliding in this Landsat 8 image from August 2016. \n\nThe collision off the coast of Mauritania produces a wave structure in the atmosphere. \n\nCalled an undular bore or solitary wave, this cloud formation was created by the interaction between cool, dry air coming off the \n\ncontinent and running into warm, moist air over the ocean. The winds blowing out from the land push a wave of air ahead like a  \n\nbow wave moving ahead of a boat. \n\nParts of these waves are favorable for cloud formation, while other parts are not. The dust blowing out from Africa appears to be \n\nriding these waves. Dust has been known to affect cloud growth, but it probably has little to do with the cloud pattern observed here.",
+      "title": "page-23.pdf",
+    },
+    {
+      "@search.score": 0.028949543833732605,
+      "@search.rerankerScore": 2.4990925788879395,
+      "chunk": "A\nT\n\nM\nO\n\nS\nP\n\nH\nE\n\nR\nE\n\nE\nA\n\nR\nT\n\nH\n\n14\n\nBering Streets\nArctic Ocean\n\nWinds from the northeast pushed sea ice southward and formed cloud streets—parallel rows of clouds—over the Bering Strait in \n\nJanuary 2010. The easternmost reaches of Russia, blanketed in snow and ice, appear in the upper left. To the east, sea ice spans \n\nthe Bering Strait. Along the southern edge of the ice, wavy tendrils of newly formed, thin sea ice predominate.\n\nThe cloud streets run in the direction of the northerly wind that helps form them. When wind blows out from a cold surface like sea \n\nice over the warmer, moister air near the open ocean, cylinders of spinning air may develop. Clouds form along the upward cycle in \n\nthe cylinders, where air is rising, and skies remain clear along the downward cycle, where air is falling. The cloud streets run toward \n\nthe southwest in this image from the Terra satellite.",
+      "title": "page-21.pdf",
+    },
+    {
+      "@search.score": 0.027637723833322525,
+      "@search.rerankerScore": 2.4686081409454346,
+      "chunk": "A\nT\n\nM\nO\n\nS\nP\n\nH\nE\n\nR\nE\n\nE\nA\n\nR\nT\n\nH\n\n38\n\nLofted Over Land\nMadagascar\n\nAlong the muddy Mania River, midday clouds form over the forested land but not the water. In the tropical rainforests of Madagascar, \n\nthere is ample moisture for cloud formation. Sunlight heats the land all day, warming that moist air and causing it to rise high into the \n\natmosphere until it cools and condenses into water droplets. Clouds generally form where air is ascending (over land in this case), \n\nbut not where it is descending (over the river). Landsat 8 acquired this image in January 2015.",
+      "title": "page-45.pdf",
+    }
+  ]
+```

@@ -14,7 +14,7 @@ ms.topic: conceptual
 ms.date: 02/28/2025
 ---
 
-# Security overview for Azure AI Search
+# Security in Azure AI Search
 
 This article describes the security features in Azure AI Search that protect data and operations.
 

@@ -2,44 +2,59 @@
 title: Use Scoring Profiles with Semantic Ranking
 titleSuffix: Azure AI Search
 description: Learn how to combine scoring profiles with semantic ranking in Azure AI Search to optimize final document relevance.
-author: gmndrg
-ms.author: gimondra
+author: HeidiSteen
+ms.author: heidist
 ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.topic: how-to
-ms.date: 06/10/2025
+ms.date: 07/22/2025
 ---
 
 # Use scoring profiles with semantic ranker in Azure AI Search
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-Integrating [scoring profiles](index-add-scoring-profiles.md) with [semantic ranker](semantic-search-overview.md) is supported in newer Azure AI Search API versions and Azure SDK packages. Semantic ranker adds a new field, `@search.rerankerBoostedScore`, to help you maintain consistent relevance and greater control over final ranking outcomes in your search pipeline.
+Using a [scoring profile](index-add-scoring-profiles.md) with [semantic ranker](semantic-search-overview.md) is supported in newer Azure AI Search preview REST API versions and Azure SDK preview packages. With this feature, the scoring profile is processed last. Without this feature, semantic ranking is processed last.
 
-Before this integration, scoring profiles only influenced the initial L1 ranking phase of [BM25-ranked](index-similarity-and-scoring.md) and [RRF-ranked](hybrid-search-ranking.md) search results. However, once the semantic L2 ranker re-ranked the results, those boosts no longer had any effect. The semantic reranking process ignored scoring profiles entirely.
+To ensure the scoring profile provides the determining score, the semantic ranker adds a new response field, `@search.rerankerBoostedScore`, that applies scoring profile logic on semantically ranked results. In search results that include `@search.score` from level 1 ranking, `@search.rerankerScore` from semantic ranker, and `@search.reRankerBoostedScore`, results are sorted by `@search.reRankerBoostedScore`.
 
-Integrating scoring profiles with semantic ranker addresses this behavior by applying scoring profiles to L2-ranked results, ensuring that the boosts are taken into account.
+> [!NOTE]
+> If you're using a stable API version or an earlier preview, scoring profiles are only used upstream, before the semantic ranking step. For a diagram of the scoring workflow, see [Relevance in Azure AI Search](search-relevance-overview.md).
 
 ## Prerequisites
 
 - [Azure AI Search](search-create-service-portal.md), Basic pricing tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
 
-- REST API version `2025-05-01-preview` or a prerelease Azure SDK package that provides the new APIs. For all preview features, we recommend reviewing the Azure SDK change logs for feature availability: [Python SDK change log](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [.NET SDK change log](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/CHANGELOG.md), [Java SDK change log](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [JavaScript SDK change log](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/search/search-documents/CHANGELOG.md).
+- [REST API version `2025-05-01-preview`](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-05-01-preview&preserve-view=true) or a preview Azure SDK package that provides the new APIs.
+
+- A search index with a semantic configuration that specifies `"rankingOrder": "boostedReRankerScore"` and a scoring profile that specifies [functions](index-add-scoring-profiles.md#use-functions).
+
+- A semantic query includes the scoring profile.
+
+> [!TIP]
+> For all preview features, we recommend reviewing the Azure SDK change logs to check for feature availability: [Python SDK change log](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [.NET SDK change log](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/CHANGELOG.md), [Java SDK change log](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [JavaScript SDK change log](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/search/search-documents/CHANGELOG.md).
+
+## Limitations
+
+Boosting of semantically ranked results applies to scoring profile functions only. There's no boosting if the scoring profile consists only of weighted text fields.
 
 ## How does semantic configuration with scoring profiles work?
 
 When you execute a semantic query associated with a scoring profile, a third search score, `@search.rerankerBoostedScore` value, is generated for every document in your search results. This boosted score, calculated by applying the scoring profile to the existing reranker score, doesn't have a guaranteed range (0–4) like a normal reranker score, and scores can be significantly higher than 4.
 
-Starting in API version `2025-05-01-preview`, semantic results are sorted by `@search.rerankerBoostedScore` by default. If the `rankingOrder` property isn't specified, then `boostedReRankerScore` is the default value in the semantic configuration.
+Starting in API version `2025-05-01-preview`, semantic results are sorted by `@search.rerankerBoostedScore` by default if a scoring profile exists. If the `rankingOrder` property isn't specified, then `BoostedReRankerScore` is the default value in the semantic configuration.
+
+In this scenario, a scoring profile is used twice. 
 
-When this capability is enabled, the scoring profile defined in your index applies during the initial ranking phase.
-It boosts results from:
+1. First, the scoring profile defined in your index is used during the initial L1 ranking phase, boosting results from:
 
-- Text-based queries (BM25 or RRF)
-- The text portion of vector queries
-- Hybrid queries that combine both types
+   - Text-based queries (BM25 or RRF)
+   - The text portion of vector queries
+   - Hybrid queries that combine both types
 
-The semantic ranker then reprocesses the top 50 results. It also reapplies the scoring profile after reranking, so your boosts influence the final order of results.
+1. Next, the semantic ranker rescores the top 50 results, promoting more semantically relevant matches to the top. This step can erase the benefit of the scoring profile. For example, if you boosted based on freshness, then semantic reordering replaces that boost with its own logic of what is most relevant.
+
+1. Finally, the scoring profile is applied again, after reranking, restoring the boosts influence over the final order of results. If you boost by freshness, the semantically ranked results are rescored based on freshness.
 
 ## Enable scoring profiles in semantic configuration
 
@@ -64,7 +79,7 @@ PUT https://{service-name}.search.windows.com/indexes/{index-name}?api-version=2
 To opt out of sorting by semantic reranker boosted score, set the `rankingOrder` field to `reRankerScore` value in the semantic configuration.
 
 ```json
-PUT https://{service-name}.search.windows.com/indexes/{index-name}?api-version=2024-05-01-Preview
+PUT https://{service-name}.search.windows.com/indexes/{index-name}?api-version=2025-05-01-Preview
 {
   "semantic": {
     "configurations": [

@@ -261,7 +261,7 @@ SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
 
 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](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 semantic ranking, the response has more elements: a new [semantically ranked relevance score](semantic-search-overview.md#how-results-are-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.
 

@@ -80,7 +80,7 @@ In semantic ranking, the query subsystem passes search results as an input to su
 
    As of November 2024, the maximum length of each generated summary string passed to the semantic ranker is 2,048 tokens. Previously, it was 256 tokens.
 
-### How ranking is scored
+## How results are scored
 
 Scoring is done over the caption, and any other content from the summary string that fills out the 2,048 token length.
 

@@ -55,21 +55,9 @@ items:
     - name: Other query types
       href: search-query-overview.md
   - name: Relevance
-    items:
-    - name: Semantic ranking
-      href: semantic-search-overview.md
-    - name: BM25 ranking
-      href: index-similarity-and-scoring.md
-    - name: Vector ranking
-      href: vector-search-ranking.md
-    - name: Hybrid ranking (RRF)
-      href: hybrid-search-ranking.md
+    href: search-relevance-overview.md
   - name: Security
-    items:
-    - name: Security overview
-      href: search-security-overview.md
-    - name: Secure access to external data
-      href: search-indexer-securing-resources.md
+    href: search-security-overview.md
 - name: Quickstarts
   items:
   - name: Connect to a search service
@@ -457,12 +445,24 @@ items:
           href: search-query-fuzzy.md
     - name: Hybrid search
       href: hybrid-search-how-to-query.md
-    - name: Ranking and relevance
+  - name: Ranking and relevance
+    items:
+    - name: BM25 ranking
       items:
-      - name: Add a scoring profile
-        href: index-add-scoring-profiles.md
+      - name: BM25 ranking overview
+        href: index-similarity-and-scoring.md
       - name: Configure BM25 ranking
         href: index-ranking-similarity.md
+    - name: Vector ranking
+      href: vector-search-ranking.md
+    - name: Hybrid ranking (RRF)
+      href: hybrid-search-ranking.md
+    - name: Add a scoring profile
+      href: index-add-scoring-profiles.md
+    - name: Semantic ranking
+      items:
+      - name: Semantic ranking overview
+        href: semantic-search-overview.md
       - name: Enable or disable semantic ranker
         href: semantic-how-to-enable-disable.md
       - name: Configure semantic ranker
@@ -499,6 +499,8 @@ items:
         href: search-security-api-keys.md
     - name: Outbound connections
       items:
+      - name: Secure access to external data
+        href: search-indexer-securing-resources.md
       - name: Configure a managed identity
         href: search-howto-managed-identities-data-sources.md
       - name: Connect using a managed identity