Chapter Nine

Designing for
AI Product
Sense

The gap between “cool demo” and “I use this every day without second-guessing” is what separates AI products that stick from ones that crater. This chapter is the playbook for the UX pillar of the Trinity.

📖 ~14 min readPages 62–68
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Mia's numbers
68%
of users opened a summary once, then ghosted. 87% eval accuracy meant nothing in real world.

Mia stared at the Slack thread, stomach twisting. Week three after launch at Pulse. The AI meeting summarizer had just hallucinated an action item. “Follow up with legal on the $1.2M expansion deal,” it assigned to the wrong director. The customer replied: “This is creative writing, not my meeting.”

Day-one feedback had been fire emojis. Retention? Cratering. 68% of users opened a summary once and ghosted. The model scored 87% on internal eval. But real users brought chaos: overlapping voices, sarcasm, context in someone's head.

This wasn't a model problem. It was a design problem. Users don't care about your ROC curve. They care whether they can rely on the thing on a random Tuesday.

AI product sense — the muscle that lets you see failure modes weeks before users do, set quality bars that matter, and design experiences that earn trust.

Chapter 9

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