diff --git a/_search-plugins/index.md b/_search-plugins/index.md
index 79e0e715d04..92628d3329b 100644
--- a/_search-plugins/index.md
+++ b/_search-plugins/index.md
@@ -24,6 +24,10 @@ OpenSearch supports the following search methods:
 
 - **Machine learning (ML)-powered search**
 
+    - **LTR search**
+        
+        - [LTR search]({{site.url}}{{site.baseurl}}/search-plugins/ltr/): Uses ML to learn a ranking function that can effectively order a set of search results based on their relevance to the user's query that are query dependent.
+    
     - **Vector search**
 
         - [k-NN search]({{site.url}}{{site.baseurl}}/search-plugins/knn/): Searches for k-nearest neighbors to a search term across an index of vectors.
diff --git a/_search-plugins/ltr/index.md b/_search-plugins/ltr/index.md
index 9675a741ec0..a58932b7f68 100644
--- a/_search-plugins/ltr/index.md
+++ b/_search-plugins/ltr/index.md
@@ -10,4 +10,42 @@ redirect_from:
 
 # LTR search
 
-Short for *Learning to Rank*, the LTR plugin enables users to be amazing!
+Short for *Learning to Rank*, the LTR plugin enables you use machine learning and behavioral data to tune the relevance of documents. 
+It uses models from the XGBoost and Ranklib libraries to rescore the search results that takes into account user behavior like click-through data that is query dependent, which can further improve relevance.
+
+[Learning to
+Rank](http://opensourceconnections.com/blog/2017/02/24/what-is-learning-to-rank/)
+applies machine learning to relevance ranking. The [OpenSearch
+Learning to Rank
+plugin](https://github.com/opensearch-project/opensearch-learning-to-rank-base)
+(OpenSearch LTR) gives you tools to train and use ranking models in
+OpenSearch. 
+
+## Get started
+
+-   Want a quickstart? Check out the demo in
+    [hello-ltr](https://github.com/o19s/hello-ltr).
+-   Brand new to learning to rank? head to
+    `core-concepts`{.interpreted-text role="doc"}.
+-   Otherwise, start with `fits-in`{.interpreted-text role="doc"}
+
+## Installing
+
+Pre-built versions can be found
+[here](https://github.com/opensearch-project/opensearch-learning-to-rank-base/releases).
+Want a build for an OS version? Follow the instructions in the [README
+for
+building](https://github.com/opensearch-project/opensearch-learning-to-rank-base#development)
+or [create an
+issue](https://github.com/opensearch-project/opensearch-learning-to-rank-base/issues).
+Once you've found a version compatible with your OpenSearch, you'd
+run a command such as:
+
+    ./bin/opensearch-plugin install https://github.com/opensearch-project/opensearch-learning-to-rank-base/releases/download/ltr-plugin-v2.11.1-RC1/ltr-plugin-v2.11.1-RC1.zip 
+
+
+
+## History
+
+The Elasticsearch LTR plugin was initially developed by [OpenSource Connections](http://opensourceconnections.com), with significant contributions by [Wikimedia Foundation](https://diff.wikimedia.org/2017/10/17/elasticsearch-learning-to-rank-plugin/), Snagajob Engineering, Bonsai, and Yelp Engineering. 
+The OpenSearch version of the plugin is derived from the Elasticsearch LTR plugin.