ux

**UX** AI user experience design requires unique considerations beyond traditional software, including setting appropriate user expectations, communicating model uncertainty, handling errors gracefully, and leveraging streaming output to improve perceived responsiveness. Setting expectations: clearly communicate what the AI can and cannot do; avoid anthropomorphizing or implying capabilities beyond the system's actual abilities. Confidence communication: when appropriate, show model uncertainty ("I'm not certain, but..."); helps users know when to double-check outputs. Error handling: AI systems will make mistakes; design for graceful degradation, easy correction, and clear feedback mechanisms. Streaming output: showing tokens as they generate feels faster than waiting for complete response; progressive disclosure maintains engagement. Explain limitations: transparent about training cutoffs, potential biases, and task types where accuracy may be limited. User control: provide mechanisms to regenerate, edit, or refine outputs; let users guide the conversation. Feedback loops: design for users to report issues, correct errors, and improve future interactions. Accessibility: ensure AI responses are accessible; consider text-to-speech, adjustable output length, and multiple modalities. The goal: AI should augment user capabilities while maintaining user agency and appropriate calibrated trust.