diff --git a/gpt4all-bindings/python/docs/gpt4all_python/monitoring.md b/gpt4all-bindings/python/docs/gpt4all_python/monitoring.md
index 59f0fa19..43809f8b 100644
--- a/gpt4all-bindings/python/docs/gpt4all_python/monitoring.md
+++ b/gpt4all-bindings/python/docs/gpt4all_python/monitoring.md
@@ -1,20 +1,20 @@
 # GPT4All Monitoring
 
-GPT4All integrates with [OpenLIT](https://github.com/openlit/openlit) open telemetry instrumentation to perform real-time monitoring of your LLM application and hardware.
+GPT4All integrates with [OpenLIT](https://github.com/openlit/openlit) OpenTelemetry auto-instrumentation to perform real-time monitoring of your LLM application and GPU hardware.
 
-Monitoring can enhance your GPT4All deployment with auto-generated traces for
+Monitoring can enhance your GPT4All deployment with auto-generated traces and metrics for
 
-- performance metrics
-
-- user interactions
-
-- GPU metrics like utilization, memory, temperature, power usage
+- **Performance Optimization:** Analyze latency, cost and token usage to ensure your LLM application runs efficiently, identifying and resolving performance bottlenecks swiftly.
+  
+- **User Interaction Insights:** Capture each prompt and response to understand user behavior and usage patterns better, improving user experience and engagement.
+  
+- **Detailed GPU Metrics:** Monitor essential GPU parameters such as utilization, memory consumption, temperature, and power usage to maintain optimal hardware performance and avert potential issues.
 
 ## Setup Monitoring
 
 !!! note "Setup Monitoring"
 
-    With [OpenLIT](https://github.com/openlit/openlit), you can automatically monitor metrics for your LLM deployment:
+    With [OpenLIT](https://github.com/openlit/openlit), you can automatically monitor traces and metrics for your LLM deployment:
 
     ```shell
     pip install openlit
@@ -25,7 +25,7 @@ Monitoring can enhance your GPT4All deployment with auto-generated traces for
     import openlit
 
     openlit.init()  # start
-    # openlit.init(collect_gpu_stats=True)  # or, start with optional GPU monitoring
+    # openlit.init(collect_gpu_stats=True)  # Optional: To configure GPU monitoring
 
     model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf')
 
@@ -38,10 +38,12 @@ Monitoring can enhance your GPT4All deployment with auto-generated traces for
         print(model.current_chat_session)
     ```
 
-## OpenLIT UI
+## Visualization
 
-Connect to OpenLIT's UI to start exploring performance metrics. Visit the OpenLIT [Quickstart Guide](https://docs.openlit.io/latest/quickstart) for step-by-step details.
+### OpenLIT UI
 
-## Grafana, DataDog, & Other Integrations
+Connect to OpenLIT's UI to start exploring the collected LLM performance metrics and traces. Visit the OpenLIT [Quickstart Guide](https://docs.openlit.io/latest/quickstart) for step-by-step details.
 
-If you use tools like , you can integrate the data collected by OpenLIT. For instructions on setting up these connections, check the OpenLIT [Connections Guide](https://docs.openlit.io/latest/connections/intro).
+### Grafana, DataDog, & Other Integrations
+
+You can also send the data collected by OpenLIT to popular monitoring tools like Grafana and DataDog. For detailed instructions on setting up these connections, please refer to the OpenLIT [Connections Guide](https://docs.openlit.io/latest/connections/intro).