AI Evaluations for Product Leaders and AI PMs

AI products are fundamentally different from traditional software—their probabilistic nature means you can't predict outputs with certainty, making traditional QA approaches insufficient. In this course, AI product leaders Chantal Cox and Aman Khan guide you through the essential practice of building evaluation systems that create trust and enable successful AI product launches. Through a conversational, podcast-style format grounded in real-world case studies from companies like LTK and Prime Video, you'll learn how to design eval strategies, implement scalable pipelines using human and model raters, and translate technical metrics into business impact. Whether you're evaluating a language generation feature, an AI agent, or a multimodal system, this course offers a complete framework for measuring what matters and making confident launch decisions.

Learning objectives
Explain why traditional QA approaches fail for AI products and how evaluation frameworks create guardrails for trust, iteration, and safe deployment of probabilistic systems.
Design comprehensive evaluation strategies for AI features by selecting appropriate metrics, datasets, and evaluation methods that map model quality to business outcomes.
Implement scalable eval pipelines using code-based evaluators, LLM-as-judge approaches, and human annotation, and determine when to use each type of rater.
Translate evaluation results into product decisions by setting launch thresholds, building monitoring dashboards, and communicating technical findings to executive stakeholders.
Create a complete evaluation plan for your own AI feature including metrics, rubrics, launch readiness criteria, and post-deployment monitoring strategies.