Evaluating and Optimizing LLM Agents

Learn to evaluate and optimize LLM agents using tools like G-Eval, DeepEval, and LangSmith. Apply metrics, build custom tests, and tune quality, cost, and latency for real-world performance and reliability

This course is designed for AI engineers, developers, and data scientists building intelligent agents who must ensure those agents produce accurate, relevant, and efficient responses—especially in complex enterprise environments. In this course, Evaluating and Optimizing LLM Agents, you’ll gain the skills needed to assess and enhance agent performance in real-world environments. First, you’ll explore core evaluation metrics like answer relevancy, hallucination rate, and contextual fit, and apply them using tools such as G-Eval and DeepEval. Next, you’ll create domain-specific test suites with open-rag-eval and build dashboards with LangSmith to monitor performance across cost, latency, and quality. Finally, you’ll learn how to apply these strategies across various architectures, including RAG agents, multi-agent systems, and chat-based tools. When you’re finished with this course, you’ll have a practical, repeatable framework for evaluating and optimizing LLM agents at scale.