Alex's changes + a few edits

Author: lgayhardt

articles/ai-studio/how-to/monitor-applications.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-studio
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 11/19/2024
+ms.date: 01/16/2025
 ms.reviewer: alehughes
 ms.author: lagayhar  
 author: lgayhardt
@@ -17,15 +17,32 @@ author: lgayhardt
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-Continuous advancements in Generative AI have led organizations to build increasingly complex applications to solve various problems (chat-bots, RAG systems, agentic systems, etc.). These applications are being used to drive innovation, improve customer experiences, and enhance decision-making. Although the models (for example, GPT-4) powering these Generative AI applications are extremely capable, continuous monitoring has never been more important to ensure high-quality, safe, and reliable results. Continuous monitoring is effective when multiple perspectives are considered when observing an application. These perspectives include token usage and cost, operational metrics – latency, request count, etc. - and, importantly, continuous evaluation. To learn more about evaluation, see [Evaluation of generative AI applications](../concepts/evaluation-approach-gen-ai.md).
+Continuous advancements in Generative AI have led organizations to build increasingly complex applications to solve various problems (chat-bots, RAG systems, agentic systems, etc.). These applications are being used to drive innovation, improve customer experiences, and enhance decision-making. Although the models (for example, GPT-4o) powering these Generative AI applications are extremely capable, continuous monitoring has never been more important to ensure high-quality, safe, and reliable results. Continuous monitoring is effective when multiple perspectives are considered when observing an application. These perspectives include token usage and cost, operational metrics – latency, request count, etc. - and, importantly, continuous evaluation. To learn more about evaluation, see [Evaluation of generative AI applications](../concepts/evaluation-approach-gen-ai.md).
 
 Azure AI and Azure Monitor provide tools for you to continuously monitor the performance of your Generative AI applications from multiple perspectives. With Azure AI Online Evaluation, you can continuously evaluate your application agnostic of where it's deployed or what orchestration framework it's using (for example, LangChain). You can use various [built-in evaluators](../concepts/evaluation-metrics-built-in.md) which maintain parity with the [Azure AI Evaluation SDK](./develop/evaluate-sdk.md) or define your own custom evaluators. By continuously running the right evaluators over your collected trace data, your team can more effectively identify and mitigate security, quality, and safety concerns as they arise, either in pre-production or post-production. Azure AI Online Evaluation provides full integration with the comprehensive suite of observability tooling available in [Azure Monitor Application Insights](/azure/azure-monitor/app/app-insights-overview), enabling you to build custom dashboards, visualize your evaluation results over time, and configure alerting for advanced application monitoring.
 
 In summary, monitoring your generative AI applications has never been more important, due to the complexity and rapid evolvement of the AI industry. Azure AI Online Evaluation, integrated with Azure Monitor Application Insights, enables you to continuously evaluate your deployed applications to ensure that they're performant, safe, and produce high-quality results in production.
 
-## Monitor your generative AI application
+## How to monitor your generative AI applications
 
-In this section, you'll learn how Azure AI integrates with Azure Monitor Application Insights to give you an out-of-the-box dashboard view that is tailored with insights regarding your generative AI app so you can stay updated with the latest status of your application.
+In this section, learn how to monitor your generative AI applications using Azure AI Foundry tracing, online evaluation, and trace visualization functionality. Then, learn how Azure AI Foundry integrates with Azure Monitor Application Insights for comprehensive observability and visualization.
+
+### Tracing your generative AI application
+
+The first step in continuously monitoring your application is to ensure that its telemetry data is captured and stored for analysis. To accomplish this, you'll need to instrument your generative AI application’s code to use the [Azure AI Tracing package](./develop/trace-local-sdk.md) to log trace data to an Azure Monitor Application Insights resource of your choice. This package fully conforms with the OpenTelemetry standard for observability. After you have instrumented your application's code, the trace data will be logged to your Application Insights resource.
+
+After you have included tracing in your application code, you can view the trace data in Azure AI Foundry or in your Azure Monitor Application Insights resource. To learn more about how to do this, see [monitor your generative AI application](monitor-applications.md#monitor-your-generative-ai-application).
+
+### Set up online evaluation
+
+After setting up tracing for your generative AI application, set up [online evaluation with the Azure AI Foundry SDK](./develop/online-evaluation.md) to continuously evaluate your trace data as it is collected. Doing so will enable you to monitor your application's performance in production over time.
+
+> [!NOTE]
+> If you have multiple AI applications logging trace data to the same Azure Monitor Application Insights resource, it's recommended to use the service name to differentiate between application data in Application Insights. To learn how to set the service name, see [Azure AI Tracing](./develop/trace-local-sdk.md). To learn how to query for the service name within your online evaluation configuration, see [using service name in trace data](./develop/online-evaluation.md#using-service-name-in-trace-data).
+
+### Monitor your generative AI application with Azure Monitor Application Insights
+
+In this section, you learn how Azure AI integrates with Azure Monitor Application Insights to give you an out-of-the-box dashboard view that is tailored with insights regarding your generative AI app so you can stay updated with the latest status of your application.
 
 ### Insights for your generative AI application  
 
@@ -63,8 +80,9 @@ You can also share this workbook with your team so they stay informed with the l
 
 ## Related content
 
+- [How to run evaluations online with the Azure AI Foundry SDK](./develop/online-evaluation.md)
 - [Trace your application with Azure AI Inference SDK](./develop/trace-local-sdk.md)
 - [Visualize your traces](./develop/visualize-traces.md)
 - [Evaluation of Generative AI Models & Applications](../concepts/evaluation-approach-gen-ai.md)
 - [Azure Monitor Application Insights](/azure/azure-monitor/app/app-insights-overview)
-- [Azure Workbooks](/azure/azure-monitor/visualize/workbooks-overview)
+- [Azure Workbooks](/azure/azure-monitor/visualize/workbooks-overview)