Examine the core differences between traditional application monitoring and the specialized requirements of LLM observability, focusing on how to make complex system failures diagnosable.

Explore critical methodologies, standard approaches, and unique challenges associated with evaluating LLM outputs and agent performance.

Compare the unique risk, cost, and iteration lifecycle factors of large language model applications against traditional machine learning systems.

Analyze the broader technology landscape and understand the technical justifications for utilizing dedicated platform solutions to monitor and evaluate AI agents.

Gain a deep understanding of the Galileo platform, including its core integrations, and complete the practical setup of your local repository and environment.

Master the mechanics of instrumentation by using Galileo's SDKs and decorators to log spans, capture agent graphs, and identify tool-use or retrieval breakdowns.

Build robust evaluation datasets, construct custom metrics for safety and correctness, and run repeatable experiments using LLM-as-judge configurations.

Consolidate your knowledge of LLM observability and evaluation practices and prepare to deploy reliable, production-grade AI agents.

Assess your ability to design LLM evaluation workflows, interpret Galileo traces and evaluation results, and select approaches for debugging and monitoring reliable AI applications.