AI evaluation framework guide for product managers

Shipping an AI feature without an AI evaluation framework risks hallucinations and angry customers. Learn to build a quality control station that catches bad outputs before launch. Just like a factory tests toasters before boxing them, product managers need a systematic way to test models. This lesson breaks the process into three steps: selecting diverse sample inputs, writing a checklist of rules, and setting a pass or fail gauge. Because AI text generation is fuzzy, your criteria must handle shades of gray. We walk through an example of evaluating AI shoe descriptions to ensure the model includes required details like size and material. Instead of just asking engineers if the model looks good, you will define exactly what the AI must always do and never do. This ensures you can accurately measure your product. In this lesson: - Picking normal and edge case samples - Writing a checklist of rules for outputs - Setting pass and fail gauges for fuzzy text - Defining what the model must and must not do U2xAI Academy - AI skills for product managers.