AI STRATEGY
Design UX to Listen and Learn
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.
Why it's Important
Enables quick learning cycles for tuning AI responses
Helps prioritize improvements based on real usage
Offers scalable signals even in low-data environments
Reduces support burden by resolving issues faster
Builds user trust by showing their feedback matters
How to Implement
Add thumbs up/down or emoji buttons near AI outputs
Create one-click survey prompts tied to key actions
Capture timestamp, prompt, output, and user feedback
Tag feedback by content type or feature
Store feedback alongside output logs for training
Display confirmation ("Thanks for your feedback!")
Track feedback trends per feature over time
Available Workshops
Micro-Feedback UI Mockup Jam
Customer Journey “Signal Point” Mapping
What Annoys Users? Roundtable
Feedback Friction Audit
Rapid Feedback Prototyping Sprint
Emoji vs. Text Sentiment Debate
Deliverables
Feedback UI mockups for key interaction points
Front-end implementation plan
Feedback data schema
List of tracked feedback types
Weekly feedback trend report
How to Measure
Feedback submission rate per user/session
Distribution of positive vs. negative feedback
Number of feedback-driven improvements shipped
Correlation between feedback and churn/retention
Bounce rate after low-score interactions
Repeat interactions from users providing negative signals
Pro Tips
A/B test feedback formats (e.g., stars vs. emojis)
Include optional free-text fields for richer input
Use AI to summarize incoming feedback at scale
Reward power users who give frequent feedback
Make feedback analytics visible in internal dashboards
Get It Right
Place buttons close to the AI response
Keep UX minimal—1 click should be enough
Thank users when feedback is received
Don’t ask for feedback too frequently
Use structured tags to group feedback types
Don't Make These Mistakes
Only collecting feedback when something goes wrong
Hiding feedback buttons behind menus
Ignoring negative feedback patterns
Failing to log metadata for analysis
Not surfacing insights to the product team