diff --git a/docs/en/reference-architectures/hot-frozen.asciidoc b/docs/en/reference-architectures/hot-frozen.asciidoc
index 8661ec2fb..68e585ca4 100644
--- a/docs/en/reference-architectures/hot-frozen.asciidoc
+++ b/docs/en/reference-architectures/hot-frozen.asciidoc
@@ -43,7 +43,7 @@ When running in your own Data Center (DC) you can equate AZs to failure zones wi
 
 The diagram illustrates an {es} cluster deployed across 3 availability zones (AZ). For production we recommend a minimum of 2 availability zones and 3 availability zones for mission critical applications. See https://www.elastic.co/guide/en/cloud/current/ec-planning.html[Plan for production] for more details. A cluster that is running in {ecloud} that has data nodes in only two AZs will create a third master-eligible node in a third AZ. High availability cannot be achieved without three zones for any distributed computing technology.
 
-The number of data nodes shown for each tier (hot and frozen) is illustrative and would be scaled up depending on ingest volume and retention period. Hot nodes contain both primary and replica shards. By default, primary and replica shards are always guaranteed to be in different availability zones in {ess}, but when self-deploying shard allocation awareness would need to be configured. Frozen nodes act as a large high-speed cache and retrieve data from the snapshot store as needed.
+The number of data nodes shown for each tier (hot and frozen) is illustrative and would be scaled up depending on ingest volume and retention period. Hot nodes contain both primary and replica shards. By default, primary and replica shards are always guaranteed to be in different availability zones in {ess}, but when self-deploying {ref}/shard-allocation-awareness.html[shard allocation awareness] would need to be configured. Frozen nodes act as a large high-speed cache and retrieve data from the snapshot store as needed.
 
 Machine learning nodes are optional but highly recommended for large scale time series use cases since the amount of data quickly becomes too difficult to analyze. Applying techniques such as machine learning based anomaly detection or Search AI with large language models helps to dramatically speed up problem identification and resolution.