Evaluating Large Language Models with Multi-Metric Techniques

Deploying large language models is only half the battle; the real challenge lies in objectively measuring their performance and accuracy. Without structured evaluation, it is impossible to know if prompt changes or model updates are genuinely improving your outputs. This written course guides you through the process of setting up and running comprehensive multi-metric evaluations, helping you move past subjective assessments to implement quantitative, reproducible testing frameworks. What you'll learn: - Understand the foundational principles of language model evaluation and why single-metric approaches fall short. - Apply traditional overlap metrics such as ROUGE and METEOR to assess text similarity. - Implement semantic evaluation techniques using BERTScore to capture contextual meaning beyond exact word matches. - Configure advanced LLM-as-a-judge frameworks like G-Eval for automated, human-aligned qualitative assessments. - Evaluate Retrieval-Augmented Generation (RAG) pipelines using the RAGAS framework to measure context precision and faithfulness. - Analyze and compare evaluation results using structured data to make informed model and prompt adjustments. The course begins with key terminology and the essential concepts of natural language processing evaluation. You will then progress through step-by-step written explanations and practical Python code snippets, learning how to implement each metric and interpret the output data effectively. This course is designed for beginners, aspiring AI engineers, data analysts, and software developers who are new to model evaluation. No prior experience with LLM testing is required, though a basic familiarity with Python will help you get the most out of the practical examples. Start building reliable, measurable AI applications today.