This LLM Evaluation Bootcamp is designed to teach developers and AI practitioners how to properly evaluate large language models (LLMs) and measure their real-world performance. As LLMs become more widely used, evaluation is a critical step in ensuring reliability, accuracy, and alignment with human expectations.
You will begin by learning the core principles of LLM evaluation, including why evaluation is necessary and how it impacts model quality and production readiness. The course introduces key ideas behind assessing generative AI systems beyond simple accuracy metrics.
Next, you will explore different evaluation methods. This includes reference-based evaluation, where outputs are compared against ground truth answers, and reference-free evaluation, where models are assessed without predefined answers. These approaches help evaluate LLMs in different real-world scenarios.
The course also covers how to design evaluation pipelines using APIs and structured workflows. You will learn how to systematically test model outputs and analyze performance.
An advanced section focuses on building an LLM judge system, which uses AI models to evaluate other AI outputs. You will also learn how to align these evaluations with human labels to improve reliability and consistency.
By the end of this course, you will understand how to evaluate, benchmark, and improve LLM performance in production systems.
This course is ideal for AI enginee
