Build feedback loops directly into your product to capture how users respond to AI outputs in real time. Lightweight tools like thumbs up/down and in-product surveys turn user behavior into fuel for AI tuning.
Let AI Analyze Its Own Feedback
Manually reviewing all user feedback is time-consuming and inconsistent. Leveraging AI to categorize, summarize, and score user responses gives you faster, more scalable insights to tune and govern your models.
Contextual feedback, such as quick surveys triggered by specific actions, lets users explain their reactions in-the-moment. You get higher-quality data when feedback feels relevant and timely.
Follow the Edits: Learn from What Users Change or Ignore
Watching how users interact with AI outputs—especially when they correct or delete them—reveals what the model gets wrong. These passive signals are often more reliable than active feedback.
Small Signals, Big Learning: Add Micro-Feedback to the Experience
Micro-feedback tools like thumbs up/down, emojis, and quick polls give users low-friction ways to signal how well the AI performed. These simple inputs provide a continuous stream of insight to refine your models and UX.