Evaluate LLMs: Test and Prove Significance

Evaluate LLMs: Test and Prove Significance is an intermediate course for ML engineers, AI practitioners, and data scientists tasked with proving the value of model updates. When making high-stakes deployment decisions, a simple accuracy score is not enough. This course equips you with the statistical methods to rigorously validate LLM performance improvements. You will learn to quantify uncertainty by calculating and interpreting confidence intervals, and to prove whether changes are meaningful by conducting formal hypothesis tests like the Chi-Square test. Through hands-on labs using Python libraries like SciPy and Matplotlib, you will analyze model outputs, test for statistical significance, and create compelling visualizations with error bars that clearly communicate your findings to stakeholders. By the end of this course, you will be able to move beyond subjective "it seems better" evaluations to confidently state, "we can prove it's better," ensuring every deployment decision is backed by sound statistical evidence

• Evaluate LLM performance using statistical tests and confidence intervals
• Analyze model outputs and conduct formal hypothesis tests
• Create visualizations that effectively communicate findings

• Basic familiarity with ML and statistics concepts
• Willingness to engage in practical exercises and case studies