Modern LLM applications introduce new classes of production challenges—silent failures, hallucinations, cost spikes, and unpredictable outputs that traditional monitoring tools cannot capture.

This course focuses on building production-grade observability and incident response systems for LLM applicationsusing LangSmith and Langfuse.

You will learn how to instrument LLM systems with structured traces, design observability schemas, monitor critical metrics like latency and cost, and debug failures using trace replay techniques. The course also introduces real-world incident response workflows, including severity classification, circuit breakers, and postmortem analysis.

By the end, you will implement a complete observability stack for LLM apps, ensuring your AI systems are reliable, debuggable, and production-ready.