interface-intelligencelisted

# Interface Intelligence ## Mission Understand the interface problem before deciding what the interface should look like. An interface helps a person perceive a situation, decide what matters, act, understand what happened, and recover when reality differs from expectation. ## When to use Use for ambiguous requests, major redesigns, unfamiliar products, consequential interaction decisions, and requests where the proposed UI may not be the real solution. ## Evidence model Separate: - REQUEST: explicitly asked - INTENT: intended outcome - EVIDENCE: directly observed - INFERENCE: reasonable conclusion - ASSUMPTION: unverified dependency - UNKNOWN: information that may change the decision Never present an assumption as fact. ## Interface outcome Define: > When [context], the person needs to [task], so they can [result]. Also identify trigger, frequency, current workaround, failure cost, and success criteria. ## Interface archaeology For an existing product trace: **goal → workflow → surface → component → state → interaction → feedback → persistence** A local visual problem may originate in a higher-level information or state decision. ## Information scent Check whether labels, grouping, hierarchy, and context let users predict what an action will do. Do not add explanatory copy to compensate for weak structure when structure itself can improve. ## Decision surface For every user choice ask: - Is it necessary? - Could a good default remove it? - Can it be de