Merge pull request #905 from HeidiSteen/heidist-fresh

[azure search] Clarified semantic ranking works with scoring profiles

Author: prmerger-automator[bot]

articles/search/index-add-scoring-profiles.md

@@ -31,6 +31,8 @@ You can create multiple profiles and then modify query logic to choose which one
 
 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.
+
 > [!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.
 >
@@ -97,9 +99,9 @@ Scoring profiles supplement the default scoring algorithm by boosting the scores
 
 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 the [*k*-matching documents](vector-search-ranking.md) include alphanumeric fields that a scoring profile can process. The scoring profile revised the result set by boosting documents that match criteria in the profile.
+For pure vectors, the query is vector-only, but if the [*k*-matching documents](vector-search-ranking.md) include alphanumeric fields that a scoring profile can process, a scoring profile is 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 (document boosting) is applied to the merged results, followed by [vector weighting](vector-search-how-to-query.md#vector-weighting), and [semantic ranking](semantic-search-overview.md) as the last step.
+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.":::