diff --git a/_monitoring-plugins/ad/api.md b/_monitoring-plugins/ad/api.md
index 46c12c28..3e7a5b8f 100644
--- a/_monitoring-plugins/ad/api.md
+++ b/_monitoring-plugins/ad/api.md
@@ -160,7 +160,7 @@ POST _plugins/_anomaly_detection/detectors
 }
 ```
 
-To create high cardinality detector by specifying a category field:
+To create a high cardinality detector by specifying a category field:
 
 #### Request
 
@@ -424,7 +424,7 @@ GET _plugins/_anomaly_detection/detectors/<detectorId>
 }
 ```
 
-Use `task=true` to get real-time analysis task information.
+Use `job=true` to get real-time analysis task information.
 
 #### Request
 
diff --git a/_monitoring-plugins/ad/index.md b/_monitoring-plugins/ad/index.md
index 6abfe5ac..6cc802e6 100644
--- a/_monitoring-plugins/ad/index.md
+++ b/_monitoring-plugins/ad/index.md
@@ -81,7 +81,7 @@ This formula provides a good starting point, but make sure to test with a repres
 
 For example, for a cluster with three data nodes, each with 8 GB of JVM heap size, a maximum memory percentage of 10% (default), and the entity model size of the detector as 1MB: the total number of unique entities supported is (8.096 * 10^9 * 0.1 / 1 MB ) * 3 = 2429.
 
-If you set the total number of unique entities higher than this number that you calculate (in this case: 2429), the anomaly detector makes its best effort to model the extra entities. The detector prioritizes entities that occur more often and are more recent.
+If the actual total number of unique entities higher than this number that you calculate (in this case: 2429), the anomaly detector makes its best effort to model the extra entities. The detector prioritizes entities that occur more often and are more recent.
 
 #### (Advanced settings) Set a shingle size