AI STRATEGY
Design UX to Listen and Learn
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.
Why it's Important
Surfaces silent dissatisfaction that feedback buttons miss
Helps improve models using real-world correction examples
Indicates which features may cause confusion or failure
Identifies usability issues and trust gaps
Provides training data without interrupting the user flow
How to Implement
Track whether users delete, override, or skip AI outputs
Compare final user version vs. AI-suggested version
Log time between output and user interaction
Use diffs or text similarity metrics to detect edits
Tag corrections by type (tone, accuracy, completeness)
Securely store edited content with opt-in and anonymization
Use corrections as part of reinforcement learning or fine-tuning
Available Workshops
Output vs. Edit Comparison Lab
Friction Log Mapping
Trust Breakage Scenario Review
Silent Signals Storyboarding
Correction Type Taxonomy Workshop
Skip Reason Brainstorm
Deliverables
Logging spec for correction/override events
Change detection script or diff utility
Taxonomy of common corrections
Dashboard of top-edited outputs
Sample dataset of user-edited outputs for model training
How to Measure
% of AI outputs edited or deleted
Edit frequency per user or feature
Common correction types (e.g., tone, fact error)
Time-to-edit after AI response
Skip rate per scenario
Similarity score between original and edited content
Correlation between edits and user satisfaction
Pro Tips
Combine passive edit logs with active thumbs down data
Use clustering to group similar types of edits
Review top-rejected outputs weekly
Highlight top corrections in team retrospectives
Create “correction of the week” for internal learning
Get It Right
Track both active and passive feedback
Include metadata for analysis (e.g., content type, persona)
Normalize logs for privacy and comparability
Use edits as training signals, not just errors
Flag frequent edits as candidates for improvement
Don't Make These Mistakes
Only focusing on explicit thumbs down
Failing to differentiate minor vs. major changes
Not aggregating corrections into themes
Ignoring high-skip outputs
Logging edits without user consent or anonymization