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Author: alaudazzi

solutions/observability/apps/llm-observability.md

@@ -8,8 +8,8 @@ While LLMs hold incredible transformative potential, they also bring complex cha
 To keep your LLM-powered applications reliable, efficient, cost-effective, and easy to troubleshoot, Elastic provides a powerful LLM observability framework including key metrics, logs, and traces, along with pre-configured, out-of-the-box dashboards that deliver deep insights into model prompts and responses, performance, usage, and costs.
 Elastic’s end-to-end LLM observability is delivered through the following methods: 
 
-- Metrics and logs ingestion for LLM APIs (via Elastic integrations)
-- APM tracing for OpenAI Models (via instrumentation)
+- Metrics and logs ingestion for LLM APIs (via [Elastic integrations](https://www.elastic.co/guide/en/integrations/current/introduction.html))
+- APM tracing for OpenAI Models (via [instrumentation](https://github.com/elastic/opentelemetry))
 
 ## Metrics and logs ingestion for LLM APIs (via Elastic integrations)
 
@@ -36,20 +36,21 @@ Check these instructions on how to setup and collect OpenTelemetry data for your
 
 ## Use cases
 
-Understand LLM performance and reliability
-For an SRE team optimizing a customer support system powered by Azure OpenAI, Elastic’s Azure OpenAI integration provides critical insights. They can quickly identify which model variants experience higher latency or error rates, enabling smarter decisions on model deployment or even switching providers based on real-time performance metrics.
+### Understand LLM performance and reliability
+
+For an SRE team optimizing a customer support system powered by Azure OpenAI, Elastic’s [Azure OpenAI integration](https://www.elastic.co/guide/en/integrations/current/azure_openai.html) provides critical insights. They can quickly identify which model variants experience higher latency or error rates, enabling smarter decisions on model deployment or even switching providers based on real-time performance metrics.
 
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 ### Troubleshoot OpenAI-powered applications
 
-Consider an enterprise utilizing an OpenAI model for real-time user interactions. Encountering unexplained delays, an SRE can use OpenAI tracing to dissect the transaction pathway, identify if one specific API call or model invocation is the bottleneck, and monitor a request to see  the exact prompt and response between the user and the LLM. Such insight is pivotal for prompt resolution.
+Consider an enterprise utilizing an OpenAI model for real-time user interactions. Encountering unexplained delays, an SRE can use OpenAI tracing to dissect the transaction pathway, identify if one specific API call or model invocation is the bottleneck, and monitor a request to see the exact prompt and response between the user and the LLM. 
 
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 ### Addressing cost and usage concerns
 
-For cost-sensitive deployments, being acutely aware of which LLM configurations are more cost-effective is crucial. Elastic’s dashboards, pre-configured to display model usage patterns, help mitigate unnecessary spending effectively. You can find out-of-the box dashboards for Azure OpenAI, OpenAI, Amazon Bedrock, and Google VertexAI models.
+For cost-sensitive deployments, being acutely aware of which LLM configurations are more cost-effective is crucial. Elastic’s dashboards, pre-configured to display model usage patterns, help mitigate unnecessary spending effectively. You can use out-of-the-box dashboards for Azure OpenAI, OpenAI, Amazon Bedrock, and Google VertexAI models.
 
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