Evaluation for LLM Applications

Learn practical LLM evaluation with error analysis, RAG systems, monitoring, and cost optimization.

Large Language Models (LLMs) are transforming the way we build applications — from chatbots and customer support tools to advanced knowledge assistants. But deploying these systems in the real world comes with a critical challenge: how do we evaluate them effectively?

This course, Evaluation for LLM Applications, gives you a complete framework to design, monitor, and improve LLM-based systems with confidence. You will learn both the theoretical foundations and the practical techniques needed to ensure your models are accurate, safe, efficient, and cost-effective.

We start with the fundamentals of LLM evaluation, exploring intrinsic vs extrinsic methods and what makes a model “good.” Then, you’ll dive into systematic error analysis, learning how to log inputs, outputs, and metadata, and apply observability pipelines. From there, we move into evaluation techniques, including human review, automatic metrics, LLM-as-a-judge approaches, and pairwise scoring.

Special focus is given to Retrieval-Augmented Generation (RAG) systems, where you’ll discover how to measure retrieval quality, faithfulness, and end-to-end performance. Finally, you’ll learn how to design production-ready monitoring, build feedback loops, and optimize costs through smart token and model strategies.

Whether you are a DevOps Engineer, Software Developer, Data Scientist, or Data Analyst, this course equips you with actionable knowledge to evaluate LLM applications in real-world environments. By the end, you’ll be ready to design evaluation pipelines that improve quality, reduce risks, and maximize value.