Overview
Curriculum
Evaluating language models goes beyond accuracy scores, it’s about building trust in their performance. In this course, we’ll go beyond surface-level accuracy and focus on what it really takes to evaluate language models effectively. Together, we’ll explore benchmarks like MMLU, agentic benchmarks, and RAGAs, and see how each plays a role in measuring performance. We’ll also practice applying these methods through hands-on exercises, so by the end, we’ll be equipped to design, analyze, and interpret evaluations with confidence in real-world scenarios.
What You'll Learn
- Understand why evaluating LLMs is essential and explore the unique challenges across different contexts
- Gain insights into popular benchmark datasets and emerging agentic benchmarks, and learn how they measure model performance
- Discover various evaluation metrics from traditional to embedding-based and know when to use each
- Build practical skills by applying evaluation techniques in hands-on exercises to design, analyze, and interpret results for real-world LLM applications
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