AI product sense is classical product sense executed under probability — every decision is a distribution, not a binary. The six lenses that capture the delta, the five-step case-round playbook, why “underserved” now means “tolerates probabilistic quality,” and the interview that probes all of it.

• →AI Product Framing

The design goal is calibrated trust, not maximal trust — over-trust is as much a defect as under-trust. Google PAIR’s input/system/context failure taxonomy, refusal-as-a-feature, citations as the trust boundary, and the recovery patterns that turn a wrong answer into a designed moment rather than an incident.

• →Trust and Failure Design

RICE still applies, but AI forces three new modifiers: confidence (eval-backed, not gut), coverage (fraction of inputs handled gracefully), and cost-of-error. The input/output metric split, eval saturation as the ship threshold, the ship-posture matrix, dynamic model routing as a prioritization move, and the discipline of pruning infinite surface area.

• →AI Prioritization

A traditional PRD is a contract for a deterministic system; AI breaks that contract twice. The shift from “output equals X” to “the eval passes >= threshold across the gold set,” the four-layer acceptance framework, numeric quality bars (not adjectives), the input/output guardrail taxonomy, and the living document that doesn’t rot by month two.

• →AI PRD Design
• →Success Criteria

The stakeholder problem with an AI bet isn’t belief — it’s that executives don’t yet know how to read a probabilistic outcome. Framing the bet as an outcome not a feature, storytelling as the AI-era survival skill, the three objections every C-suite AI PRD must pre-empt, and the decision log that answers “why this model?” in 15 seconds.

• →Executive AI Narrative

Assemble the whole track into one artifact: run the product-sense case for an agentic personal-shopping assistant, then write its PRD — goals/non-goals, the agent architecture choice, eval set, guardrails, failure modes, and the exec narrative — rehearsing the decisions an interviewer (or an incident) will push on.

• →AI Product Framing
• →AI PRD Design
• →Executive AI Narrative