fix warnings

Author: lgayhardt

articles/ai-studio/how-to/online-evaluation.md

@@ -26,7 +26,7 @@ In this section, you'll learn how Azure AI Online Evaluation works, how it integ
 
 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-your-generative-ai-application).
+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).
 
 ### Online Evaluation
 

articles/ai-studio/toc.yml

@@ -344,7 +344,7 @@ items:
   - name: Deploy and monitor generative AI apps
     items:
     - name: Continuously monitor your applications
-      href: how-to/monitor-application.md
+      href: how-to/monitor-applications.md
     - name: Deploy and monitor flows
       items:
       - name: Deploy a flow for real-time inference