Evaluating Large Language Models: Tasks, Benchmarks, and Metrics

Deploying large language models requires more than just prompting; you must understand how to measure their accuracy, reasoning, and safety. This text-based course guides you through the fundamental methodologies used to test and validate AI models systematically. You will transition from guessing how well a model performs to executing structured evaluations. You will learn to interpret major industry benchmarks, design custom evaluation tasks, and apply modern assessment techniques to align models with specific business needs. What you'll learn: Understand foundational evaluation concepts, terminology, and the limitations of automated metrics; Analyze core evaluation tasks including natural language understanding, reasoning, and summarization; Explore industry-standard benchmark datasets such as MMLU, HELM, and Big-Bench Hard (BBH); Evaluate Retrieval-Augmented Generation (RAG) systems for faithfulness and context relevance; Implement LLM-as-a-judge patterns to evaluate open-ended model outputs; Design structured test suites to prevent regression and monitor model performance over time. The course begins with essential terminology and the core mechanics of model testing before guiding you through standard benchmarks, custom evaluation design, and modern RAG testing patterns. This course is designed for beginner AI developers, product managers, and data analysts looking to build reliable AI applications, with no advanced mathematical prerequisites required. Start reading today to bring rigorous, data-driven evaluation to your language model workflows.