AI User Feedback Analysis Template: Turn Feedback into Product Insights
Copy-paste template for systematically categorizing, analyzing, and prioritizing user feedback on AI features. Build a feedback loop that drives meaningful improvements.
By Institute of AI PM|8 min read|Dec 15, 2025
AI features generate unique feedback patterns. Users struggle to articulate why AI outputs feel wrong, confuse model limitations with bugs, and have expectations shaped by other AI products. This template helps you systematically capture, categorize, and act on AI-specific feedback.
AI Feedback Taxonomy
Why AI needs its own taxonomy: Traditional feedback categories (bug, feature request, UX issue) don't capture AI-specific problems like hallucinations, inconsistent outputs, or misaligned user expectations.
┌─────────────────────────────────────────────────────────────────┐ │ AI FEEDBACK TAXONOMY │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ CATEGORY 1: OUTPUT QUALITY │ │ ├── Accuracy Issues │ │ │ ├── Factually incorrect │ │ │ ├── Outdated information │ │ │ └── Hallucination/fabrication │ │ ├── Relevance Issues │ │ │ ├── Off-topic response │ │ │ ├── Missed user intent │ │ │ └── Over/under-specific │ │ └── Quality Issues │ │ ├── Poorly formatted │ │ ├── Too verbose/too brief │ │ └── Inconsistent style │ │ │ │ CATEGORY 2: BEHAVIOR & CONSISTENCY │ │ ├── Inconsistency │ │ │ ├── Different outputs for same input │ │ │ ├── Contradicts previous response │ │ │ └── Behavior changed unexpectedly │ │ ├── Boundary Issues │ │ │ ├── Refused valid request │ │ │ ├── Allowed harmful request │ │ │ └── Inconsistent refusals │ │ └── Context Issues │ │ ├── Lost conversation context │ │ ├── Wrong persona/tone │ │ └── Ignored user preferences │ │ │ │ CATEGORY 3: PERFORMANCE │ │ ├── Speed │ │ │ ├── Response too slow │ │ │ ├── Streaming lag │ │ │ └── Timeout/failure │ │ └── Reliability │ │ ├── Partial response │ │ ├── Error message shown │ │ └── Feature unavailable │ │ │ │ CATEGORY 4: USER EXPERIENCE │ │ ├── Interaction Design │ │ │ ├── Unclear how to use │ │ │ ├── Can't correct/refine │ │ │ └── Missing undo/regenerate │ │ ├── Feedback & Control │ │ │ ├── Can't rate outputs │ │ │ ├── No explanation provided │ │ │ └── Can't adjust settings │ │ └── Trust & Transparency │ │ ├── Unclear what AI can do │ │ ├── No confidence indicator │ │ └── Sources not provided │ │ │ │ CATEGORY 5: FEATURE REQUESTS │ │ ├── New Capabilities │ │ │ ├── Support new use case │ │ │ ├── Handle new input type │ │ │ └── New output format │ │ ├── Integration │ │ │ ├── Connect to X service │ │ │ ├── Export to X format │ │ │ └── API access │ │ └── Personalization │ │ ├── Remember preferences │ │ ├── Custom instructions │ │ └── Domain-specific tuning │ │ │ └─────────────────────────────────────────────────────────────────┘
Feedback Log Template
┌─────────────────────────────────────────────────────────────────┐ │ FEEDBACK LOG ENTRY │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ENTRY ID: [FB-YYYY-MM-###] │ │ DATE RECEIVED: [YYYY-MM-DD] │ │ SOURCE: [ ] In-app [ ] Support [ ] Survey [ ] Social │ │ [ ] User interview [ ] Analytics [ ] Other: ___ │ │ │ │ ───────────────────────────────────────────────────────────── │ │ USER CONTEXT │ │ ───────────────────────────────────────────────────────────── │ │ │ │ User Segment: [Free / Pro / Enterprise / Internal] │ │ Experience Level: [New (<1mo) / Regular / Power user] │ │ Use Case: [________________________] │ │ Feature Used: [________________________] │ │ │ │ ───────────────────────────────────────────────────────────── │ │ FEEDBACK DETAILS │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Verbatim Feedback: │ │ ┌───────────────────────────────────────────────────────────┐ │ │ │ │ │ │ │ [Paste exact user quote here] │ │ │ │ │ │ │ └───────────────────────────────────────────────────────────┘ │ │ │ │ User Input (if available): │ │ ┌───────────────────────────────────────────────────────────┐ │ │ │ │ │ │ │ [What did the user ask/input?] │ │ │ │ │ │ │ └───────────────────────────────────────────────────────────┘ │ │ │ │ AI Output (if available): │ │ ┌───────────────────────────────────────────────────────────┐ │ │ │ │ │ │ │ [What did the AI respond?] │ │ │ │ │ │ │ └───────────────────────────────────────────────────────────┘ │ │ │ │ User's Expected Output: │ │ ┌───────────────────────────────────────────────────────────┐ │ │ │ │ │ │ │ [What did the user want instead?] │ │ │ │ │ │ │ └───────────────────────────────────────────────────────────┘ │ │ │ │ ───────────────────────────────────────────────────────────── │ │ CLASSIFICATION │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Primary Category: [Output Quality / Behavior / Performance / │ │ UX / Feature Request] │ │ Sub-Category: [________________________] │ │ Specific Issue: [________________________] │ │ │ │ Sentiment: [ ] Negative [ ] Neutral [ ] Positive │ │ Severity: [ ] Critical [ ] High [ ] Medium [ ] Low│ │ Frequency: [ ] One-off [ ] Recurring [ ] Trending │ │ │ │ ───────────────────────────────────────────────────────────── │ │ ANALYSIS │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Root Cause Hypothesis: │ │ [________________________] │ │ │ │ Is this a: │ │ [ ] Model limitation [ ] Prompt issue │ │ [ ] Data/training gap [ ] UX/expectation gap │ │ [ ] Bug [ ] Edge case │ │ [ ] Working as intended [ ] Unclear │ │ │ │ Related Feedback IDs: [FB-xxx, FB-xxx] │ │ Similar Issues Count: [__] in last 30 days │ │ │ │ ───────────────────────────────────────────────────────────── │ │ ACTION │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Recommended Action: │ │ [ ] No action needed [ ] Monitor for pattern │ │ [ ] Quick fix [ ] Add to backlog │ │ [ ] Escalate to ML team [ ] Update documentation │ │ [ ] User education [ ] Feature consideration │ │ │ │ Priority Score: [__] / 10 │ │ Assigned To: [________________________] │ │ Target Resolution: [YYYY-MM-DD / Backlog / Won't fix] │ │ │ │ Notes: │ │ [________________________] │ │ │ └─────────────────────────────────────────────────────────────────┘
Weekly Summary Template
┌─────────────────────────────────────────────────────────────────┐ │ AI FEEDBACK WEEKLY SUMMARY │ │ Week of: [YYYY-MM-DD] │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ VOLUME METRICS │ │ ───────────────────────────────────────────────────────────── │ │ Total Feedback Items: [___] (vs last week: +/-__%) │ │ Unique Users: [___] (vs last week: +/-__%) │ │ Feedback Rate: [___]% of active users │ │ │ │ BY SOURCE: │ │ ├── In-app: [___] (___%) │ │ ├── Support: [___] (___%) │ │ ├── Survey: [___] (___%) │ │ └── Other: [___] (___%) │ │ │ │ ───────────────────────────────────────────────────────────── │ │ CATEGORY BREAKDOWN │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Output Quality: [███████████░░░░] 45% ([___] items) │ │ Behavior/Consistency: [████████░░░░░░░] 25% ([___] items) │ │ Performance: [████░░░░░░░░░░░] 12% ([___] items) │ │ User Experience: [███░░░░░░░░░░░░] 10% ([___] items) │ │ Feature Requests: [██░░░░░░░░░░░░░] 8% ([___] items) │ │ │ │ ───────────────────────────────────────────────────────────── │ │ SENTIMENT TREND │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Positive: [██████░░░░░░░░░] 20% (vs last week: +/-__%) │ │ Neutral: [█████████░░░░░░] 35% (vs last week: +/-__%) │ │ Negative: [████████████░░░] 45% (vs last week: +/-__%) │ │ │ │ Net Sentiment Score: [___] (-100 to +100) │ │ │ │ ───────────────────────────────────────────────────────────── │ │ TOP ISSUES THIS WEEK │ │ ───────────────────────────────────────────────────────────── │ │ │ │ 1. [Issue description] │ │ Category: [___] | Count: [___] | Severity: [___] │ │ Status: [New / Investigating / In Progress / Resolved] │ │ │ │ 2. [Issue description] │ │ Category: [___] | Count: [___] | Severity: [___] │ │ Status: [New / Investigating / In Progress / Resolved] │ │ │ │ 3. [Issue description] │ │ Category: [___] | Count: [___] | Severity: [___] │ │ Status: [New / Investigating / In Progress / Resolved] │ │ │ │ ───────────────────────────────────────────────────────────── │ │ NOTABLE QUOTES │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Positive: │ │ "[User quote that highlights what's working well]" │ │ — [User segment], [Feature] │ │ │ │ Needs Improvement: │ │ "[User quote that highlights key pain point]" │ │ — [User segment], [Feature] │ │ │ │ ───────────────────────────────────────────────────────────── │ │ ACTIONS TAKEN │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Resolved This Week: │ │ • [Issue] — [Resolution] │ │ • [Issue] — [Resolution] │ │ │ │ In Progress: │ │ • [Issue] — [Status/ETA] │ │ • [Issue] — [Status/ETA] │ │ │ │ Added to Backlog: │ │ • [Issue] — [Priority] │ │ │ │ ───────────────────────────────────────────────────────────── │ │ RECOMMENDATIONS FOR NEXT WEEK │ │ ───────────────────────────────────────────────────────────── │ │ │ │ 1. [Recommendation with rationale] │ │ 2. [Recommendation with rationale] │ │ 3. [Recommendation with rationale] │ │ │ └─────────────────────────────────────────────────────────────────┘
Feedback Prioritization Matrix
AI prioritization differs from traditional product feedback. A single hallucination reported by one user might indicate a systemic issue affecting thousands. Weight AI safety and trust issues higher than typical bugs.
┌─────────────────────────────────────────────────────────────────┐ │ PRIORITIZATION SCORING │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ IMPACT SCORE (0-10) │ │ ───────────────────────────────────────────────────────────── │ │ │ │ User Impact: │ │ • Affects core workflow: +3 │ │ • Affects secondary feature: +1 │ │ • Cosmetic/minor inconvenience: +0 │ │ │ │ Reach: │ │ • Affects >50% of users: +3 │ │ • Affects 10-50% of users: +2 │ │ • Affects <10% of users: +1 │ │ • Single user report: +0 │ │ │ │ AI-Specific Multipliers: │ │ • Safety/harmful content risk: x2.0 │ │ • Trust/credibility damage: x1.5 │ │ • Legal/compliance concern: x2.0 │ │ • Data privacy issue: x2.0 │ │ │ │ ───────────────────────────────────────────────────────────── │ │ EFFORT SCORE (0-10, lower = easier) │ │ ───────────────────────────────────────────────────────────── │ │ │ │ Fix Type: │ │ • Prompt adjustment only: 1-2 │ │ • UI/UX change: 2-4 │ │ • Model fine-tuning: 5-7 │ │ • Architecture change: 8-10 │ │ • Requires new model/capability: 9-10 │ │ │ │ Dependencies: │ │ • Self-contained: +0 │ │ • Needs ML team: +2 │ │ • Needs external vendor: +3 │ │ • Needs new data: +3 │ │ │ │ ───────────────────────────────────────────────────────────── │ │ PRIORITY MATRIX │ │ ───────────────────────────────────────────────────────────── │ │ │ │ EFFORT │ │ Low High │ │ ┌───────────┬───────────┐ │ │ High │ DO NOW │ PLAN IT │ │ │ IMPACT │ P0-P1 │ P1-P2 │ │ │ ├───────────┼───────────┤ │ │ Low │ QUICK │ AVOID │ │ │ │ WINS │ (MAYBE) │ │ │ └───────────┴───────────┘ │ │ │ │ Priority Levels: │ │ P0 - Drop everything (safety, major outage) │ │ P1 - This sprint (high impact, user trust) │ │ P2 - Next sprint (important but not urgent) │ │ P3 - Backlog (nice to have) │ │ P4 - Won't do (low value or infeasible) │ │ │ └─────────────────────────────────────────────────────────────────┘
Common AI Feedback Patterns
The Expectation Gap
Pattern: Users expect AI to do X, but it only does Y.
Signal: "I thought it would..." or "Why can't it just..."
Action: Often a documentation/onboarding fix, not a model fix. Set expectations clearly upfront.
The Inconsistency Complaint
Pattern: Same input gives different outputs.
Signal: "Yesterday it worked..." or "Sometimes it does X, sometimes Y"
Action: Investigate if this is temperature/sampling, context window limits, or actual model regression.
The Power User Edge Case
Pattern: Advanced users push boundaries, find limitations.
Signal: Detailed technical feedback, specific failure modes.
Action: Valuable signal for model improvement. Engage these users directly for more context.
The Silent Churn Signal
Pattern: Users stop using feature without explicit feedback.
Signal: Usage drop, regenerate spikes, session abandonment.
Action: Proactive outreach, session recordings, exit surveys. Most AI dissatisfaction is silent.
AI Feedback Collection Best Practices
┌─────────────────────────────────────────────────────────────────┐ │ FEEDBACK COLLECTION CHECKLIST │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ IN-APP FEEDBACK │ │ ───────────────────────────────────────────────────────────── │ │ [ ] Thumbs up/down on AI outputs │ │ [ ] "Report issue" option with categories │ │ [ ] "This was helpful" confirmation │ │ [ ] Optional free-text field │ │ [ ] Capture input/output pair automatically │ │ [ ] Include session context (not just single turn) │ │ │ │ IMPLICIT SIGNALS TO TRACK │ │ ───────────────────────────────────────────────────────────── │ │ [ ] Regenerate button clicks │ │ [ ] Edit/modify after AI output │ │ [ ] Copy vs. ignore output │ │ [ ] Time spent reading output │ │ [ ] Follow-up queries (correction attempts) │ │ [ ] Session abandonment points │ │ │ │ PROACTIVE COLLECTION │ │ ───────────────────────────────────────────────────────────── │ │ [ ] Post-task satisfaction surveys │ │ [ ] Weekly NPS for AI features specifically │ │ [ ] User interviews with screen sharing │ │ [ ] Beta tester feedback channels │ │ [ ] Power user advisory group │ │ │ │ DATA TO CAPTURE WITH FEEDBACK │ │ ───────────────────────────────────────────────────────────── │ │ [ ] Timestamp │ │ [ ] User segment / plan │ │ [ ] Feature / model version │ │ [ ] Full conversation context │ │ [ ] Device / platform │ │ [ ] Response latency │ │ [ ] Token count (input/output) │ │ │ │ PRIVACY CONSIDERATIONS │ │ ───────────────────────────────────────────────────────────── │ │ [ ] PII scrubbing before storage │ │ [ ] User consent for feedback collection │ │ [ ] Clear data retention policy │ │ [ ] Option to delete feedback │ │ [ ] Anonymization for analysis │ │ │ └─────────────────────────────────────────────────────────────────┘
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