A prompt is not a magic incantation — it is the most concentrated piece of product behavior a designer ever ships. How one clause changes what users feel, the four-part anatomy you design slot by slot, real leaked system prompts (Claude, v0, Notion) as case studies, and the interview probe that opens every AI-design loop.

• →Prompt Behavior Design

Context engineering is now a design discipline: every component you put in the window — retrieved docs, memory, the current date, tool definitions — is a UI surface with its own latency, freshness, and trust cost. What RAG, memory, and function-calling actually change about the experience, the patterns that earn trust (citations, confidence, provenance), and how to reason about them in a case round.

• →Context and Memory UX

The system message is the most concentrated brand voice a designer ever writes — and refusals, error states, and empty states are where AI products win or lose trust. Real system-message text and the UX it produces, the refusal-as-design-pattern (substitute, not scold), voice constraints that leak from the delivery pipeline, and why Klarna had to walk back AI-first support.

• →AI Voice and System Copy

A probabilistic feature has no single correct output, so the deterministic metrics you trust actively mislead. Write the rubric before the prompt, split Core (zero-tolerance) from feature-specific criteria, reframe A/B testing as uncertainty reduction, and pick the few UX + model metrics that tie to a user-visible property — the exact evaluation reasoning AI-design loops weight at 25% of the score.

• →AI UX Evaluation

AI prototyping is three layers, not one tool — Figma for shape, Framer/Magic Patterns for on-brand demo, code+LLM (v0/Bolt/Lovable) for behavioral truth. The Wizard-of-Oz move for validating an interaction before a model exists, when to really wire the model, the streaming + skeleton patterns that make probabilistic UIs feel responsive, and how Notion’s design team actually prototypes.

• →Behavioral AI Prototyping

Put it together: design an end-to-end AI interaction with a real prompt strategy and a human-in-the-loop review/approve flow where the human is also the evaluator. The five HITL patterns, confidence-based escalation, the review-gate numbers, provisional states as a system, and the portfolio piece that clears every AI-design rubric at once.

• →Review and Approval Flows
• →Prompt Behavior Design
• →AI UX Evaluation