diff --git a/docs/en/ml/aggregations.asciidoc b/docs/en/ml/aggregations.asciidoc
index 7739dd142c0..cc98a45d11e 100644
--- a/docs/en/ml/aggregations.asciidoc
+++ b/docs/en/ml/aggregations.asciidoc
@@ -9,8 +9,6 @@ One of the benefits of aggregating data this way is that {es} automatically
 distributes these calculations across your cluster. You can then feed this
 aggregated data into {xpackml} instead of raw results, which
 reduces the volume of data that must be considered while detecting anomalies.
-//TBD: Are "aggregated" and "summarized" equivalent terms? Are customers more
-//familiar with one or the other? If so, I'll use one term throughout.
 
 There are some limitations to using aggregations in {dfeeds}, however.
 Your aggregation must include a buckets aggregation, which in turn must contain
@@ -95,7 +93,12 @@ field is also `time`. The same is true for the aggregations with the names
 `airline` and `responsetime`. Since you must create the job before you can
 create the {dfeed}, synchronizing your aggregation and field names can simplify
 these configuration steps.
-//TBD: Describe how this would be accomplished in Kibana?
+
+IMPORTANT: If you use a `max` aggregation on a time field, the aggregation name
+in the {dfeed} must match the name of the time field, as in the previous example.
+For all other aggregations, if the aggregation name doesn't match the field name,
+there are limitations in the drill-down functionality within the {ml} page in
+{kib}.
 
 When you define an aggregation in a {dfeed}, it must have the following form:
 
@@ -175,8 +178,6 @@ parent aggregation. For more information, see
 TIP: If your detectors use metric or sum analytical functions, set the
 `interval` of the date histogram aggregation to a tenth of the `bucket_span`
 that was defined in the job. This suggestion creates finer, more granular time
-buckets, which are ideal for this type of analysis. If your detectors use count or rare functions, set
-`interval` to the same value as `bucket_span`. For more information about
-analytical functions, see <<ml-functions>>.
-
-//TBD: Add more examples from https://github.com/elastic/prelert-legacy/wiki/Configuring-aggregations-on-a-datafeed
+buckets, which are ideal for this type of analysis. If your detectors use count
+or rare functions, set `interval` to the same value as `bucket_span`. For more
+information about analytical functions, see <<ml-functions>>.