AI Feedback Loop
Qarion collects structured feedback on every AI-generated suggestion — descriptions, field classifications, quality check recommendations, and anomaly explanations. This feedback loop helps administrators measure AI accuracy and identify areas for improvement.
Providing Feedback
Inline Actions
When the AI generates a suggestion (e.g., a product description), you see inline feedback controls:
- Thumbs Up — The suggestion is accurate as-is
- Thumbs Down — The suggestion is not useful
- Accept — Apply the suggestion directly
- Edit — Modify the suggestion before applying (the original and edited values are both recorded)
- Reject — Dismiss the suggestion entirely
Every action is logged along with the AI feature that produced it, the associated AI log entry, and an optional comment explaining why the suggestion was accepted or rejected.
Adding Comments
When giving a thumbs down, editing, or rejecting a suggestion, you can add a free-text comment. These comments are especially valuable for understanding patterns — they help identify whether suggestions are failing due to missing context, incorrect assumptions, or formatting issues.
Administrator Dashboard
Feedback Stats
Navigate to Administration → AI Feedback to view aggregated statistics across all AI features:
| Metric | Description |
|---|---|
| Total Count | Total number of feedback entries for the period |
| Acceptance Rate | Percentage of positive outcomes (thumbs up + accepted + edited) |
| Thumbs Up / Down | Count of each rating action |
| Accepted / Edited / Rejected | Count of each outcome action |
Stats can be filtered by feature and by time period (last 7, 14, or 30 days).
Exporting Data
Click Export to download all feedback entries as CSV or JSON. Exports include every field: feedback ID, feature, action, comment, AI log reference, user ID, original value, edited value, and timestamp. This data is useful for fine-tuning prompts, identifying systematic inaccuracies, and reporting on AI adoption metrics.
Supported Features
Feedback collection is integrated into the following AI features:
| Feature | Feedback Trigger |
|---|---|
| Description Generation | After generating a product description |
| Field Description | After generating a column-level description |
| Tag Suggestions | After AI suggests classification tags |
| Quality Check Suggestions | After AI recommends quality rules |
| Anomaly Explanation | After generating a root-cause hypothesis |
| AI Classification | After bulk field classification suggestions |
Related
- AI Copilot Overview — How the AI Copilot works
- Token Usage & Cost Monitoring — Track AI consumption
- Anomaly Explanation — AI-powered alert root-cause analysis