Advanced11 min
Feedback Pipelines
Build closed-loop feedback systems that capture user signals (thumbs up/down, corrections, regenerations), process them into actionable data, and drive measurable improvements to prompts, retrieval, and model selection.
Quick Reference
- →Capture explicit signals (thumbs up/down, corrections) and implicit signals (regenerations, copy events, session abandonment)
- →Signal processing: filter noise, aggregate by topic, detect patterns — not every thumbs-down is meaningful
- →Convert feedback into three outputs: prompt improvements, retrieval tuning, and fine-tuning datasets
- →Closed-loop validation: measure whether feedback-driven changes actually improve quality with A/B tests
- →Anonymize all feedback data before storage — strip PII, hash user IDs, aggregate to cohorts
Capturing User Feedback Signals
Explicit vs implicit signals
Explicit feedback (thumbs up/down, star ratings) has high signal but low volume — only 2-5% of users provide it. Implicit feedback (regenerations, copy-paste, time-on-page, session abandonment) has lower signal per event but 100x more volume. You need both.
| Signal Type | Source | Strength | Volume | Interpretation |
|---|---|---|---|---|
| Thumbs up | UI button | Strong positive | Low (2-3% of responses) | Response met user need |
| Thumbs down | UI button | Strong negative | Low (1-2% of responses) | Response failed — need correction category |
| Correction text | Feedback form | Very strong | Very low (<0.5%) | User tells you exactly what was wrong |
| Regeneration | Retry button | Moderate negative | Medium (5-10%) | First response was unsatisfactory |
| Copy event | Clipboard API | Moderate positive | Medium | User found the response useful enough to copy |
| Session abandonment | Analytics | Weak negative | High | User gave up — many possible causes |
| Follow-up question | Conversation flow | Context-dependent | High | May indicate incomplete answer or natural conversation |
Feedback event capture with structured metadata