Emerging & AI UX

Practical principles for building interfaces around large language models — covering mental models, hybrid UI, failure states, and trust architecture.

AI can now render the interface itself at runtime — understanding when, why, and how to design these systems safely is the defining skill of the next decade.

Designing conversational interfaces and AI-powered prompts that reduce friction, set accurate expectations, and keep humans in meaningful control.

How to design AI interfaces that build calibrated user trust through honest capability communication, graceful failure, and mental model alignment.

Designing for AI agents that act autonomously demands new interaction patterns — confirmation gates, interrupt controls, scope boundaries, and transparent undo — to keep humans meaningfully in control.

Designing voice interfaces demands a fundamentally different mental model — one built on conversation flow, audio feedback, and graceful error recovery.

Designing interfaces that fluidly combine voice, touch, gaze, gesture, and text demands a new set of principles far beyond stacking input channels together.

Calibrating user mental models from the first interaction is the highest-leverage design decision in any AI-powered product.

Designing interfaces that surface AI failures honestly, help users recover gracefully, and preserve trust when models confidently produce wrong answers.

Designing AI products that earn trust through honest data practices, meaningful consent, and user-legible explanations of how their information shapes model behavior.

Designing for LLMs demands rethinking response delivery — how you expose context limits, stream tokens, and signal uncertainty shapes user trust and task success more than answer quality alone.

Designing oversight, transparency, and control into systems where multiple AI agents collaborate autonomously on a user's behalf.

Designing the prompts behind an AI feature — system prompts, context engineering, structured output, guardrails, and evaluation.