AI Evaluations for Product Leaders and AI PMs

AI Evaluations for Product Leaders and AI PMs is a compact introduction to the evaluation responsibilities product leaders cannot delegate away. In 28 minutes, Aman Khan and Chantal Cox explain why probabilistic AI products need a different quality approach from traditional software and show how evaluations connect failure analysis, datasets, human judgment, automated scoring, launch decisions, and executive communication. The course uses a conversational, podcast-style format and draws on product examples including LTK and Prime Video, making it useful when you want a fast orientation before committing to a longer technical program.

The course begins by defining AI evaluation and examining the trust, safety, and business risks created when model behaviour cannot be predicted with certainty. It then walks through the first practical steps: selecting an evaluation approach across the product lifecycle, identifying likely failure modes, and creating an initial dataset. A section on scalable systems compares human labelling, code-based evaluators, and LLM-as-judge techniques. You also see common mistakes in evaluation prompts and how evaluation choices need to change as a product moves from early exploration toward launch and ongoing iteration.

Its strongest PM-specific material comes in the section that turns metrics into decisions. The instructors address how to decide whether an AI feature is ready to launch, how to judge whether evaluation coverage is sufficient, how to report results to leadership, and how to iterate using product metrics after release. The final exercise asks you to build an evaluation plan, so the course gives you a small but concrete artifact rather than only a vocabulary list. A LinkedIn Learning certificate is available on completion.

This is best for product managers, group PMs, and product leaders who have worked with AI or LLM features but lack a structured evaluation method. The intermediate label is fair, although the short runtime means it functions more as an accessible first rung than a complete operating playbook. You will not leave with deep implementation skills or extensive practice. You will leave with a coherent map of the decisions an AI product leader must own and a clearer basis for collaborating with engineers, data scientists, reviewers, legal partners, and executives. It is particularly useful as pre-work before a hands-on eval course or before planning a new AI feature.