LLM Evaluation for Builders

LLM Evaluation for Builders is a practical LLM evaluation course for people who need to understand how quality is measured in real AI applications. Evidently AI structures the program as a free, three-week sequence of video lessons and ten hands-on Python tutorials. Instead of stopping at high-level frameworks, the course takes you through the workflows used to test summarisation, classification, content generation, RAG systems, and basic agents. It is a strong technical step up for product managers who already understand why evaluations matter and now want to work more effectively with machine learning and engineering partners.

You learn how to build evaluation datasets, compare prompts and models, trace model outputs, and turn observed failures into automated checks. The curriculum covers deterministic validations alongside model-based scoring, then shows how to design and tune custom LLM judges so their scores reflect human preferences and actual failure modes. Dedicated material on RAG separates retrieval quality from generation quality and explains where synthetic data can help. Adversarial testing adds the safety and edge-case perspective that teams often leave until late in development. The course also addresses collaborative debugging and how a team can organise test cases rather than treating evaluation as one specialist's private task.

The intended audience includes AI and ML engineers, technical product managers, and hands-on builders working on LLM applications. You will get more from the exercises if you can read and run Python code; this is not a no-code overview. At the same time, the progression is accessible enough for a technically curious PM who wants to understand the artifacts, trade-offs, and vocabulary used by an evaluation team. The course is self-paced, while live Q&A sessions provide an optional opportunity to clarify implementation questions.

By the end, you should be able to discuss evaluation with more precision: what dataset represents the product's risk, which checks should be deterministic, when an LLM judge is appropriate, how to compare candidate prompts or models, and what traces reveal when aggregate scores hide a problem. The value for a PM is not becoming a full-time evaluation engineer. It is gaining enough applied experience to challenge weak quality claims, scope credible acceptance criteria, and help a cross-functional team build an evaluation loop that supports product and launch decisions.