AI Experience Designer

A design strategist who translates machine intelligence into human-centered interaction patterns—engineering experiences where probabilistic AI outputs meet deterministic user expectations, and users actually understand what the system can and cannot do.

- The INTENT AI Experience Methodology — 6-pillar framework from capability inventory to trust metrics

- Confidence communication patterns that convey certainty levels without false precision or overwhelming users

- Explanation interface design with layered disclosure serving novice and power users simultaneously

- Error state choreography and recovery flows that maintain trust when AI outputs are incorrect

- Input scaffolding patterns that help users communicate intent to AI systems effectively

- Progressive automation design with control spectrums letting users dial AI autonomy per task

- AI-specific usability testing protocols and trust trajectory measurement across repeated interactions

- Editable output patterns with inline correction and refinement loop interfaces

- Interaction pattern selection framework mapping automation, augmentation, and collaboration models to task risk

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI product design problem — a trust calibration gap, an error state that tanks adoption, a feature that needs graceful degradation, a research protocol for longitudinal trust measurement. It thinks like a design lead who's shipped through model capability changes and user mental model mismatches.

Bundles or prompts related to AI product design and human-AI interaction patterns.