Learn LLM evaluation methods to systematically test, measure, and improve AI applications with practical frameworks.
LLM evaluation is what separates AI products that work from ones that just seem to.
This course gives you a repeatable process for testing AI outputs, finding errors, and fixing them fast.
Most teams building AI products rely on gut checks. They change a prompt, eyeball a few outputs, and ship it.
This course replaces that guesswork with a system.
You get a structured approach to evaluating AI applications — from error analysis and data annotation to automated testing pipelines.
It covers how to build LLM-as-a-judge evaluators you can actually trust, how to generate synthetic data when you have no customers yet, and how to debug RAG systems and multi-step agents.
The material covers text, image, and audio use cases. It also addresses team dynamics — how to align evals with stakeholders, avoid common organizational pitfalls, and set up CI/CD evaluation gates. Four optional coding assignments with solutions let you practice hands-on.
What You Will Learn
Build custom LLM-as-a-judge and code-based evaluators using a systematic, iterative process.
Apply data analysis techniques to find systematic errors in AI outputs across any use case.
Set up automated evaluation gates in CI/CD pipelines.
Debug RAG systems for retrieval relevance and factual accuracy.
Generate synthetic data to bootstrap evaluation when you have no user data.
Ideal for: engineers and PMs building AI products who need a reliable way to measure and improve output quality.