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Co-authored-by: Kathleen DeRusso <[email protected]>

docs/reference/reranking/index.asciidoc

@@ -3,19 +3,18 @@
 
 Many search systems are built on two-stage retrieval pipelines.
 
-The first stage uses cheap, fast algorithms to find a subset of possible matches.
+The first stage uses cheap, fast algorithms to find a broad set of possible matches.
 
 The second stage uses a more powerful model, often machine learning-based, to reorder the documents.
 This second step is called re-ranking.
-This approach balances computational costs, because the resource-intensive model is only applied to a smaller set of pre-filtered results.
+Because the resource-intensive model is only applied to the smaller set of pre-filtered results, this approach returns more relevant results while still optimizing for search performance and computational costs.
 
 {es} supports various ranking and re-ranking techniques to optimize search relevance and performance.
 
 [float]
 [[re-ranking-two-stage-pipeline]]
 == Two-stage retrieval pipelines
 
-Learn about retrieval pipelines and how re-ranking can be applied to your existing search experience.
 
 [float]
 [[re-ranking-first-stage-pipeline]]
@@ -26,7 +25,7 @@ Learn about retrieval pipelines and how re-ranking can be applied to your existi
 ==== Full-text search: BM25 scoring
 
 {es} ranks documents based on term frequency and inverse document frequency, adjusted for document length.
-BM25 is the default statistical scoring algorithm in {es} and works out-of-the-box.
+https://en.wikipedia.org/wiki/Okapi_BM25[BM25] is the default statistical scoring algorithm in {es} and works out-of-the-box.
 
 [float]
 [[re-ranking-ranking-overview-vector]]
@@ -42,7 +41,7 @@ Refer to <<semantic-search,semantic search>> for more information.
 ==== Hybrid techniques
 
 Hybrid search techniques combine results from full-text and vector search pipelines.
-{es} enables combining lexical matching (BM25) and vector search scores using the advanced <<rrf,Reciprocal Rank Fusion (RRF)>> algorithm.
+{es} enables combining lexical matching (BM25) and vector search scores using the <<rrf,Reciprocal Rank Fusion (RRF)>> algorithm.
 
 [float]
 [[re-ranking-overview-second-stage]]
@@ -57,15 +56,15 @@ These candidates are funneled into the re-ranker to perform more computationally
 
 <<semantic-reranking>> uses machine learning models to reorder search results based on their semantic similarity to a query.
 Models can be hosted directly in your {es} cluster, or you can use <<inference-apis,inference endpoints>> to call models provided by third-party services.
-Enables out-of-the-box semantic search capabilities on existing full-text search indices.
+Semantic re-ranking enables out-of-the-box semantic search capabilities on existing full-text search indices.
 
 [float]
 [[re-ranking-overview-ltr]]
 ==== Learning to Rank (LTR)
 
 <<learning-to-rank>> is for advanced users.
-Train a machine learning model to build a ranking function for your search experience that updates over time.
-Best suited for when you have ample training data and need highly customized relevance tuning.
+Learning To Rank involves training a machine learning model to build a ranking function for your search experience that updates over time.
+LTR is best suited for when you have ample training data and need highly customized relevance tuning.
 
 include::semantic-reranking.asciidoc[]
 include::learning-to-rank.asciidoc[]