diff --git a/docs/reference/search/retriever.asciidoc b/docs/reference/search/retriever.asciidoc
index b86339b905631..db59a7769c36d 100644
--- a/docs/reference/search/retriever.asciidoc
+++ b/docs/reference/search/retriever.asciidoc
@@ -209,6 +209,11 @@ GET /index/_search
 
 The `text_similarity_reranker` is a type of retriever that enhances search results by re-ranking documents based on semantic similarity to a specified inference text, using a machine learning model.
 
+[TIP]
+====
+Refer to <<semantic-reranking>> for a high level overview of semantic reranking.
+====
+
 ===== Prerequisites
 
 To use `text_similarity_reranker` you must first set up a `rerank` task using the <<put-inference-api, Create {infer} API>>.
diff --git a/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc b/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc
index 48cd735981382..2d5135b885c93 100644
--- a/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc
+++ b/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc
@@ -5,7 +5,7 @@ preview::[]
 
 [TIP]
 ====
-This overview focuses more on the high-level concepts and use cases for semantic reranking. For full implementation details on how to set up and use semantic reranking in {es}, see the <<retriever,reference documentation>> in the Search API docs.
+This overview focuses more on the high-level concepts and use cases for semantic reranking. For full implementation details on how to set up and use semantic reranking in {es}, see the <<text-similarity-reranker-retriever,reference documentation>> in the Search API docs.
 ====
 
 Rerankers improve the relevance of results from earlier-stage retrieval mechanisms.