diff --git a/docs/reference/elasticsearch/rest-apis/retrievers.md b/docs/reference/elasticsearch/rest-apis/retrievers.md
index 717410fe3e48f..c0cb3b2f8a53e 100644
--- a/docs/reference/elasticsearch/rest-apis/retrievers.md
+++ b/docs/reference/elasticsearch/rest-apis/retrievers.md
@@ -560,11 +560,11 @@ Refer to [*Semantic re-ranking*](docs-content://solutions/search/ranking/semanti
 
 ### Prerequisites [_prerequisites_15]
 
-To use `text_similarity_reranker` you must first set up an inference endpoint for the `rerank` task using the [Create {{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put). The endpoint should be set up with a machine learning model that can compute text similarity. Refer to [the Elastic NLP model reference](docs-content://explore-analyze/machine-learning/nlp/ml-nlp-model-ref.md#ml-nlp-model-ref-text-similarity) for a list of third-party text similarity models supported by {{es}}.
+To use `text_similarity_reranker`, you can rely on the preconfigured `.rerank-v1-elasticsearch` inference endpoint, which is based on [Elastic Rerank](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-rerank.html) and serves as the default if no `inference_id` is provided. This model is optimized for reranking based on text similarity. If you'd like to use a different model, you can set up a custom inference endpoint for the `rerank` task using the [Create {{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put). The endpoint should be configured with a machine learning model capable of computing text similarity. Refer to [the Elastic NLP model reference](docs-content://explore-analyze/machine-learning/nlp/ml-nlp-model-ref.md#ml-nlp-model-ref-text-similarity) for a list of third-party text similarity models supported by {{es}}.
 
 You have the following options:
 
-* Use the the built-in [Elastic Rerank](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put) cross-encoder model via the inference API’s {{es}} service.
+* Use the built-in [Elastic Rerank](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put) cross-encoder model via the inference API’s {{es}} service. For an example of creating an endpoint using the Elastic Rerank model, refer to [this guide](https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-service-elasticsearch.html#inference-example-elastic-reranker).
 * Use the [Cohere Rerank inference endpoint](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put) with the `rerank` task type.
 * Use the [Google Vertex AI inference endpoint](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put) with the `rerank` task type.
 * Upload a model to {{es}} with [Eland](eland://reference/machine-learning.md#ml-nlp-pytorch) using the `text_similarity` NLP task type.
@@ -606,9 +606,9 @@ score = ln(score), if score < 0
 
 
 `inference_id`
-:   (Required, `string`)
+:   (Optional, `string`)
 
-    Unique identifier of the inference endpoint created using the {{infer}} API.
+    Unique identifier of the inference endpoint created using the {{infer}} API. If you don’t specify an inference endpoint, the `inference_id` field defaults to `.rerank-v1-elasticsearch`, a preconfigured endpoint for the elasticsearch `.rerank-v1` model.
 
 
 `inference_text`