AI Evals: Test LLM Apps, RAG and Agents Like an Engineer

About this course

This course contains the use of artificial intelligence.

Building an LLM application is easy. Knowing whether it actually works is much harder.

This course teaches you how to evaluate, test, debug, and improve AI systems built with large language models, Retrieval-Augmented Generation (RAG), and AI agents. Instead of relying on a few hand-picked prompts and deciding that an output “looks good,” you will learn how to approach AI evaluation like an engineer: define measurable criteria, build evaluation datasets, detect regressions, analyze failures, and make evidence-based decisions about your system.

You will explore why evaluating generative AI is fundamentally different from traditional software testing. LLM outputs are probabilistic, multiple answers can be valid, and a response can sound convincing while still being incorrect. Throughout the course, you will learn how to turn these challenges into structured and repeatable evaluation workflows.

A major part of the course focuses on LLM-as-a-Judge. You will learn how model-based evaluators work, where they are useful, and the biases and failure modes that can make evaluation scores misleading. You will design evaluation rubrics, compare model outputs, measure qualitative characteristics, and understand when automated evaluation should be combined with deterministic checks and human judgment.

For RAG systems, you will go beyond evaluating only the final generated answer. You will examine retrieval quality, context relevance, answer correctness, groundedness, and faithfulness so you can determine whether a failure originated from the retriever, the supplied context, the generation step, or the underlying knowledge base.

You will also learn how evaluation changes when working with AI agents. Agentic systems introduce tool calls, intermediate decisions, multi-step workflows, and trajectories that cannot be properly evaluated by checking only the fin

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