Evaluating Large Language Models: Metrics and Methods

Deploying language models requires more than just generating text; you need to know if those outputs are accurate, safe, and high-quality. Understanding how to systematically evaluate these models is the key to building reliable AI applications. This written course guides you through the foundational concepts and modern methodologies of Large Language Model (LLM) evaluation. You will transition from manual, ad-hoc testing to designing robust evaluation pipelines using both traditional statistical metrics and cutting-edge LLM-assisted evaluation frameworks. What you'll learn: - Understand foundational evaluation terminology and the difference between intrinsic and extrinsic metrics. - Apply classic NLP metrics such as perplexity, BLEU, and ROUGE to assess text generation quality. - Evaluate Retrieval-Augmented Generation (RAG) systems using metrics for faithfulness and context relevance. - Implement the LLM-as-a-judge pattern to automate complex qualitative assessments. - Analyze industry-standard benchmark datasets and understand their limitations in real-world scenarios. - Design systematic evaluation workflows to guide continuous model improvement and alignment. The course begins with core definitions and traditional evaluation metrics before moving into advanced topics like RAG evaluation and automated grading techniques. You will practice these concepts through conceptual written exercises and code-based scenarios. This course is designed for AI enthusiasts, software developers, and data analysts who are new to model evaluation and want to build a solid, structured understanding without needing complex prerequisites. Start reading today to master the art of measuring and refining language model performance.