model
Adaptive AI feedback loop
A human-centered AI product should collect useful feedback during interaction and use it to improve future behavior.
正文
An adaptive AI feedback loop treats the user interaction as a two-way system. The product observes corrections, dismissals, preferences, and successful outcomes, then uses that evidence to refine defaults, prompts, examples, or future recommendations.
For early-stage products, the loop can stay simple: capture what the user changed, why they changed it, and whether the next generated result became more useful.
来源引用
People + AI Guidebook patterns
Source: People + AI Guidebook patterns
Google PAIR frames human-AI interaction as a feedback loop where systems can adapt from user interaction over time.
相关卡片
Feedback share card
A feedback share card packages an idea as a short public question instead of a polished announcement.
Correction affordance for AI output
AI output should be easy to edit, refine, undo, or recover from when it is wrong.
AI-assisted iteration cycle
AI-assisted iteration works best when generation is paired with feedback, scoring, and reusable learning.
所在阅读路径
Human-centered AI feedback loop
A route for keeping AI tools useful after first generation: map risks, support correction, capture feedback, and review production readiness.