diff --git a/docs/my-website/release_notes/v1.81.14.md b/docs/my-website/release_notes/v1.81.14.md
index 694d2ccf3b6..66dbaa29ef1 100644
--- a/docs/my-website/release_notes/v1.81.14.md
+++ b/docs/my-website/release_notes/v1.81.14.md
@@ -54,14 +54,12 @@ pip install litellm==1.81.14
 
 Browse built-in and partner guardrails organized by use case — competitor blocking, topic filtering, keyword lists, GDPR/EU AI Act compliance, prompt injection detection, and more. Pick a template, customize the parameters (keyword lists, blocked topics, score thresholds), and attach it to a team or key.
 
+![Guardrail Garden](../img/release_notes/guardrail_garden.png)
+
 ## 3 New Built-in Guardrails
 
 A competitor name blocker, a topic blocker (keyword and embedding-based), and an insults/keyword filter. All run at the gateway level with no external API call. They're configurable per-team or per-key, and you can swap in AWS Bedrock Guardrails or Azure Content Safety on the same endpoint without changing your application code.
 
-## Compliance Playground
-
-Upload your own dataset or use a pre-built one to measure how a guardrail policy performs before it goes live. See precision, recall, and false positive rate on your actual traffic patterns — so you know how the policy will behave in production before you deploy it.
-
 ### Eval results
 
 We benchmark every built-in guardrail against labeled datasets before shipping. Results for the two policies most relevant to topic and keyword blocking (207 investment-question cases, 299 insult cases):
@@ -73,6 +71,14 @@ We benchmark every built-in guardrail against labeled datasets before shipping.
 
 For reference, ONNX embedding approaches on the same eval set hit 95–98% precision at 2–20ms latency and require additional dependencies. The built-in content filter uses no ML model — just structured YAML rules with layered matching — so there's nothing to download, no API key needed, and latency is effectively zero.
 
+## Compliance Playground
+
+Upload your own dataset or use a pre-built one to measure how a guardrail policy performs before it goes live. See precision, recall, and false positive rate on your actual traffic patterns — so you know how the policy will behave in production before you deploy it.
+
+![Compliance Playground](../img/release_notes/compliance_playground.png)
+
+
+
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