What I will learn?

• Understand the critical need for observability in large language model (LLM) applications and the challenges associated with unmonitored LLM behavior.
• Gain hands-on experience using Arize Phoenix for real-time monitoring, traceability, and evaluation of LLM outputs.
• Learn to identify and measure key performance indicators (KPIs) like coherence, hallucination rate, and drift in LLM-based systems.
• Master the ingestion and structuring of LLM trace data (spans, traces, and events) to facilitate transparent and meaningful analysis.
• Explore advanced LLMOps workflows and observability techniques using Arize Phoenix’s architecture, integrations, and MCP Server.