LLM Evals & Agent Evals

LLM Evals and Agent Evals is a comprehensive, hands-on course designed to help you master the evaluation of LLM-powered systems, from selecting the right model to testing RAG applications and AI agents in development and production. You will learn why LLM applications require a dedicated evaluation discipline and how it differs from testing traditional software, particularly when dealing with probabilistic outputs, multi-dimensional quality, and the limits of informal “vibe testing.”

Starting with the fundamentals, you will learn what an eval really is and follow a structured evaluation workflow built around success criteria and golden datasets. You will explore the three evaluation methods (programmatic, human, and LLM-as-a-judge), along with reference-based vs. reference-free and offline vs. online evaluation, and understand why a single application needs evals at multiple levels and across quality, safety, and operational risks.

You will then dive into model evals: the core capabilities of LLMs, how benchmarks and eval harnesses work, and how to read leaderboards critically. Through a hands-on case study, you will run a custom model evaluation on your own data, weighing accuracy, cost, and latency to select the right model for a real application.

Moving to application evals, you will build a RAG application with LangChain and Chroma and evaluate it component by component using DeepEval. You will measure retriever and generator quality, evaluate the full pipeline with the RAG triad, and use G-Eval to assess application quality such as correctness, completeness, and style. The course also covers safety evaluation, including toxicity, data leakage, and scope adherence, as well as operational evaluation of latency, cost, and reliability.

Moving beyond individual evals, you will learn regression testing to ensure that improvements are measurable and do not introduce regressions elsewhere. You will then implement online evaluation with LangSmith, learning to trace production traffic, build dashboards and alerts, evaluate live responses, and feed real-world failures back into your evaluation datasets.

In the agent evals module, you will learn a first-principles framework for evaluating any AI agent, from defining its capabilities and failure modes to choosing the right metrics. Working with an agent built in LangGraph, you will evaluate task completion, planning, and tool use, track operational performance through tracing, and test safety and robustness against risks such as data loss, prompt injection, and ambiguous instructions.

By the end of the course, you will be equipped to select the right model, design and run complete evaluation suites, and keep LLM applications reliable across both RAG-based and agentic AI systems.

Course Duration: 35+ hours

Refund Policy: 4 Days