AI STRATEGY
Design UX to Listen and Learn
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.
Why it's Important
Transforms noisy feedback into structured insight
Reveals themes and sentiment trends in real time
Reduces review effort for product teams
Enables prioritization of high-impact issues
Keeps feedback analysis consistent and unbiased
How to Implement
Use LLMs to summarize free-text feedback by topic or theme
Classify sentiment (positive, negative, neutral) using AI models
Auto-tag feedback with labels (e.g., "fact error," "off-topic")
Score urgency or severity based on signal patterns
Feed scored feedback into dashboards for triage
Combine structured and unstructured feedback sources
Evaluate clustering results against manually labeled examples
Available Workshops
Feedback Label Ideation Session
Prompt Engineering for Clustering
Sentiment Tagging Simulation
Feedback Funnel Mapping
Triaging Exercise: AI vs. Human Prioritization
Real vs. AI-Summarized Feedback Review
Deliverables
Prompt templates for summarizing or classifying feedback
List of standardized feedback labels
Feedback clustering model (or integration with optimized LLM models)
Dashboards showing issue frequency, sentiment, and urgency
Sample feedback transcripts with AI-generated summaries
How to Measure
Precision and recall of AI classifications vs. human review
Reduction in manual triage time
Coverage rate of labeled feedback
Number of issues flagged by AI before human detection
Time from feedback to insight
Stakeholder satisfaction with feedback visibility
% of high-severity feedback resolved per sprint
Pro Tips
Highlight “Top 3 Feedback Themes” weekly
Use model confidence scores to flag uncertain labels
Include “what changed” summaries in releases
Build feedback summaries into sprint planning
Share sentiment trendlines in investor or board updates
Get It Right
Fine-tune prompts using your dataset
Continuously validate AI tagging against real-world results
Balance qualitative nuance with quantitative clarity
Use human QA for critical issues
Share clustered findings with cross-functional teams
Don't Make These Mistakes
Blindly trusting AI labels without validation
Ignoring false positives in theme detection
Using too many labels without clear definitions
Failing to update prompt templates over time
Keeping feedback insights siloed from product teams