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Week 6: Decision-Making and Organization · Lesson 6.2

Translating evaluation signals to product actions

Given what we observed, what should we change next, and how will we know it helped?

Retired course. Due to the fast pace of AI, this course was retired before full release. Exercises, datasets, and videos referenced in this lesson are not available. The slide content and frameworks remain free to study.

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Reader Notes

The v2 change shipped based on experiment results. The metrics look green. But users are complaining. That gap, green dashboard but angry users, is the subject of this entire lesson. The goal is to learn how to detect when behavioral signals conflict with evaluation metrics, and more importantly, how to map those conflicts to specific, testable interventions. Not vague fixes. Testable ones. With hypotheses. With validation plans. With priority scores. By the end of this lesson, the framework translates observations into fixes. It covers how to look at a user complaint, trace it back to a metric gap, propose an intervention, and design a test to validate whether the intervention actually worked. This is the lesson where the focus shifts from analyzing metrics to fixing the system based on what the metrics reveal. Every lesson before this was about building the measurement infrastructure. Now it gets used. The emphasis is on action, not just measurement.

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