Grounding evaluation in user value

Welcome to Week 3, Lesson 1. This week shifts from building instrumentation to validating that metrics actually matter. The signals have been captured; now they connect to user value. The question driving this entire lesson: How do you know whether a metric that looks good on a dashboard actually predicts whether users find the system useful? That gap, between technical quality and user value, is where most AI product failures live. Teams build dashboards full of green checkmarks and assume users are happy. They're not. They're confused, frustrated, or abandoning sessions while metrics say everything is fine. This lesson teaches how to distinguish between metrics that predict user outcomes and metrics that just look rigorous. It introduces a three-layer framework: leading metrics, lagging metrics, and business metrics. It then shows how to validate the connections between them using correlation studies on production traces. By the end, the distinction will be clear: which metrics earn the right to be release gates, which are useful for optimization but not ship decisions, and which ones are measurement theater, precise numbers that tell you nothing about whether users care. That distinction changes how every ship decision gets made going forward.