Dec ai native patterns

AI-Native Design Engineering

The new layer, increasingly central to the role. Probabilistic systems need UX that plans for being wrong.

• Generative / Adaptive UI: Interfaces assembled or personalized at runtime by a model rather than fully predetermined.
• Streaming as Interaction: Token-by-token output, partial results, and stop/regenerate controls are core interaction patterns, not edge cases.
• Prompt-as-Interface: Natural language as an input modality alongside (not replacing) direct manipulation — with affordances that teach users what the system can do.
• Human-in-the-Loop / Agentic UX: Design review, approval, correction, and undo around autonomous actions; surface confidence and provenance.
• Eval-Driven Development: Treat model behavior as a testable surface — eval sets and regression checks for AI features, analogous to visual regression for UI.
• Designing for Uncertainty: Probabilistic outputs need graceful failure, transparent limitations, and easy escape hatches. The system will be wrong; the UX plans for it.
• Latency & Cost as Design Constraints: Model choice, caching, and streaming are UX decisions, not just infra ones.

How to apply

• Make streaming first-class: show partial output immediately, always offer stop and regenerate.
• For agentic actions, design the review/approve/undo loop before the autonomous capability — surface confidence and provenance.
• Teach the user what the system can do via input affordances, not a blank prompt box.
• Build an eval set early and run it like a regression suite; "looks good once" is not a test.
• Plan the wrong-answer path (graceful failure + escape hatch) as carefully as the happy path.
• Treat latency/cost/model choice as UX levers — cache, stream, and pick the smallest model that meets the bar.