This introductory module lays the groundwork for quantitative Large Language Mode (LLM) evaluation. Learners will discover why relying on intuition to judge model performance is unsustainable and explore the foundational metrics used to create automated, objective evaluation systems. We will cover both lexical similarity metrics (like BLEU and ROUGE-L) that assess text structure and semantic metrics (like cosine similarity) that capture meaning. By the end of this module, learners will have the conceptual understanding and practical code to build their first automated evaluation script.

This module transitions from raw metrics to credible conclusions. Learners will discover why statistical rigor is non-negotiable when comparing LLM outputs. They will learn to formulate clear hypotheses, design and analyze A/B tests, and interpret results such as p-values and confidence intervals to distinguish true performance gains from random noise. By the end of this module, learners will be equipped to make data-driven decisions with confidence, ensuring that changes to prompts, models, or parameters lead to statistically significant improvements.

This module transitions from raw metrics to credible conclusions. Learners will discover why statistical rigor is non-negotiable when comparing LLM outputs. They will learn to formulate clear hypotheses, design and analyze A/B tests, and interpret results such as p-values and confidence intervals to distinguish true performance gains from random noise. By the end of this module, learners will be equipped to make data-driven decisions with confidence, ensuring that changes to prompts, models, or parameters lead to statistically significant improvements.