troduces the essential concepts and methods used to evaluate large language model applications in real-world environments. It is designed for learners who want to understand how to measure, test, and improve the performance of AI systems beyond simple accuracy metrics.

You will begin by understanding what an LLM-powered product is and how it differs from traditional software systems. The course explains how generative AI applications behave in dynamic and probabilistic ways, making evaluation more complex and important.

Next, you will explore LLM evaluation benchmarks, which are standardized tests used to measure model quality and compare performance across different systems. These benchmarks help developers understand strengths and weaknesses in AI models.

The course also covers how to evaluate LLM applications in practice. You will learn structured approaches for testing outputs, identifying errors, and improving reliability in production systems.

A key section focuses on evaluation datasets, including test cases and synthetic data generation. These techniques help simulate real-world usage scenarios and improve testing coverage.

By the end of this course, you