ai-feedback-loopslisted

# AI Feedback Loops Design feedback mechanisms that simultaneously improve the AI model AND improve the user's experience of giving feedback. The SIGNAL framework ensures feedback is low-friction, high-quality, and genuinely acted upon. ## Core Principle Feedback is a **transaction.** Users invest effort (reporting an error, rating an output, explaining what's wrong). If they never see a return on that investment, they stop giving feedback. Design feedback loops that close - where users can see that their input made a difference. --- ## The SIGNAL Framework | Letter | Principle | Design Question | |---|---|---| | **S** | Surface the Moment | Is feedback offered at the exact moment the user has an opinion? | | **I** | Incentivize Honestly | Does the feedback design encourage genuine assessment, not just positive ratings? | | **G** | Graduate the Effort | Can users give 1-second feedback OR 1-minute feedback, depending on their willingness? | | **N** | Narrate the Impact | Can users see how their feedback improved the system? | | **A** | Aggregate Intelligently | Is individual feedback combined into actionable patterns, not just counted? | | **L** | Loop the Learning | Does the improved AI behavior visibly reflect the feedback it received? | --- ## The Feedback Pyramid Design feedback collection in layers - most users will only reach the first layer, and that's fine. | Layer | Effort | Signal Quality | Collection Rate | Mechanism | |---|---|---|---|---| | **L1: Implic