diff --git a/docs/reference/reranking/semantic-reranking.asciidoc b/docs/reference/reranking/semantic-reranking.asciidoc
index 5050729f86f46..e1e2abd224a8e 100644
--- a/docs/reference/reranking/semantic-reranking.asciidoc
+++ b/docs/reference/reranking/semantic-reranking.asciidoc
@@ -88,8 +88,8 @@ To use semantic re-ranking in {es}, you need to:
 . *Select and configure a re-ranking model*.
 You have the following options:
 .. Use the <<inference-example-elastic-reranker,Elastic Rerank>> cross-encoder model via the inference API's {es} service. 
-.. Integrate directly with the <<infer-service-cohere,Cohere Rerank inference endpoint>> using a `rerank` endpoint.
-.. Integrate directly with the <<infer-service-google-vertex-ai,Google Vertex AI inference endpoint>> using a `rerank` endpoint.
+.. Use the <<infer-service-cohere,Cohere Rerank inference endpoint>> to create a `rerank` endpoint.
+.. Use the <<infer-service-google-vertex-ai,Google Vertex AI inference endpoint>> to create a `rerank` endpoint.
 .. Upload a model to {es} from Hugging Face with {eland-docs}/machine-learning.html#ml-nlp-pytorch[Eland]. You'll need to use the `text_similarity` NLP task type when loading the model using Eland. Then set up an <<inference-example-eland,{es} service inference endpoint>> with the `rerank` endpoint type.
 + 
 Refer to {ml-docs}/ml-nlp-model-ref.html#ml-nlp-model-ref-text-similarity[the Elastic NLP model reference] for a list of third party text similarity models supported by {es} for semantic re-ranking.