diff --git a/README.md b/README.md
index c747b038..7bce8011 100644
--- a/README.md
+++ b/README.md
@@ -188,3 +188,6 @@ By default, Grafana application is deployed in `llm-monitoring` namespace.To lau
   - Ratings are displayed for each model.
 
 ![Routes](images/monitoring.png)
+
+## Test Plan
+GOTO: [Test Plan](./TESTPLAN.md)
diff --git a/TESTPLAN.md b/TESTPLAN.md
new file mode 100644
index 00000000..7c64db61
--- /dev/null
+++ b/TESTPLAN.md
@@ -0,0 +1,93 @@
+# Test Cases for E2E Demo.
+
+- [Provisioning of GPU Node](https://github.com/validatedpatterns-sandbox/rag-llm-gitops/blob/main/GPU_provisioning.md)
+
+  - MachineSet is created.
+    - Name of the machine set <clustername>-gpu-<AWSregion>. This should not be a hard requirement though. 
+    - Machine set has taint section
+      ```yaml
+      taints:
+        - effect: NoSchedule
+          key: odh-notebook  <--- Use own taint name or skip all together
+          value: 'true'
+      ```
+
+    - MachineSet has a label
+      ```yaml
+       metadata:
+         labels:
+           node-role.kubernetes.io/odh-notebook: '' <--- Put your label if needed
+      ```
+
+    - MachineSet instance type
+      ```yaml
+       providerSpec:
+         value:
+         ........................
+         instanceType: g5.2xlarge <---- Change vm type if needed
+      ```
+
+    - The nodes are provisioned with the proper label. The number of pods running should be greater than 20.
+      ![labeled nodes](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-label-nodes.png)
+
+    - NVIDIA pods should be running on the nodes. Check the pods running on the GPU nodes.
+      ![nvidia pods](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-pattern-running-pods.png)
+
+  - Verify Node Feature Discovery Operator is installed:
+    - Select Installed Operators from the left Navigation Bar and under Projects, select All Projects. Node Discover Feature Operator should be installed
+      ![nfd operator](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nfd-operator.png)
+
+    - Click on the Node Feature Discovery Operator. Under NodeFeatureDiscovery an instance should be created. Status should be Available.
+      ![nfd instance](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nfd-instance.png)
+
+
+  - Verify NVIDIA GPU Operator is installed.
+    - NVIDIA GPU Operator is installed
+      
+      ![nvidia operator](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nvidia-operator.png)
+
+    - Click on the NVIDIA GPU Operator and click on ClusterPolicy. A gpu-cluster-policy should exist
+      ![nvidia clusterpolicies](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nvidia-clusterpolicies.png)    
+
+    - Click on the gpu-cluster-policy and click on the YAML tab. The YAML should contain the tolerations
+      ```yaml
+       tolerations:
+         - effect: NoSchedule
+           key: odh-notebook
+           value: 'true'
+      ```
+
+- Application provisioned correctly.
+  - Click on the rag-llm namespace
+    - Four pods should be running
+      ![ragllm pods](https://validatedpatterns.io/images/rag-llm-gitops/rag-llm.png)
+  
+    - Click on Networking → Routes from the left Navigation panel. An llm-ui route should exist
+      ![llm-ui route](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-application_route.png)
+
+    - Click on the link under Location column and it should launch the application
+      ![llm-ui application](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-model-application.png)
+
+    - Enter customer name as ‘IBM’ and for product enter ‘RedHat OpenShift’ and click Generate. A project proposal should be generated
+      ![llm-ui project](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-application-running.png)
+
+    - Click on Ratings to rate the model.
+
+- Verify Grafana and Prometheus are installed correctly.
+  - By default, Grafana application is deployed in llm-monitoring namespace.To launch the Grafana Dashboard, follow the instructions below:
+    - Grab the credentials of Grafana Application
+      - Navigate to Workloads --> Secrets
+      - Click on the grafana-admin-credentials and copy the `GF_SECURITY_ADMIN_USER`, `GF_SECURITY_ADMIN_PASSWORD`
+    - Launch Grafana Dashboard
+      - Navigate to Networking --> Routes in the llm-monitoring namespace.
+      - Click on the Location link for grafana-route.
+      - Enter the Grafana admin credentials.
+      - Ratings are displayed for each model
+    - Grafana Dashboard is displayed
+      
+      ![llm-ui grafana](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-grafana.png)
+
+
+
+	
+