Apply suggestions from code review

Co-authored-by: Barry Eom <[email protected]>

content/en/llm_observability/guide/llm_observability_and_apm.md

@@ -12,15 +12,18 @@ further_reading:
 
 ## Overview
 
-The LLM Observability SDK is built on APM's `dd-tracer`. This allows you to use LLM Observability with [Application Performance Monitoring (APM)][7]. You can link LLM Observability and APM [spans][6] in Datadog by instrumenting your LLM-specific operations with LLM Observability and your broader application with APM.
+This guide explains how you can use both LLM Observability and APM to link LLM Observability and APM [spans][6] in Datadog. 
+
+By instrumenting your LLM-specific operations with LLM Observability and your broader application with APM, you can accomplish the following:
+
 
-This integration allows you to accomplish the following:
 
 * Understand end-to-end visibility: Explore upstream and downstream requests of your LLM applications within the context of your entire application.
 * From APM, dive deeper into LLM Observability: Investigate whether or not an issue with your application is specific to LLM-specific applications, such as a call to OpenAI.
 
 ## Setup
 
+The LLM Observability SDK is built on APM's dd-tracer. This allows you to use LLM Observability with [Application Performance Monitoring (APM)][7]
 If you are using the [LLM Observability SDK for Python][1] along with APM's [`dd-tracer`][2], you can navigate between spans in Datadog APM and LLM Observability without additional setup.
 
 If you are using the [LLM Observability API][3] with `dd-tracer` for APM: