AI Agent Evaluation Fundamentals: Understanding Evals

Building AI agents requires more than just coding; it demands a robust understanding of how to assess their performance and reliability. Without proper evaluation, AI agents can be unpredictable and fail to meet expectations in real-world scenarios. This course provides a foundational understanding of AI agent evaluation techniques, equipping you with the knowledge to systematically test, analyze, and improve your agent's behavior. What you'll learn: Understand the core principles and terminology of AI agent evaluation. Learn to differentiate and apply various evaluation types, including single-turn, offline, and online methods. Apply strategies for evaluating non-deterministic AI agent behaviors and outputs. Practice designing relevant evaluation metrics and creating effective test cases for agent performance. Explore the impact of prompt engineering and context on robust agent evaluation. Understand foundational concepts for evaluating Retrieval-Augmented Generation (RAG) agent components. The course begins with foundational concepts and terminology, then progresses to practical methods for designing and conducting evaluations, and concludes with strategies for interpreting results and improving agent performance. This course is designed for beginners interested in AI agents and their development, with no prior experience in evaluation methodologies required. Start building more robust and dependable AI agents today.