AI Squared allows you to capture direct feedback from business users who interact with AI model outputs embedded through Data Apps. This feedback is essential for evaluating model relevance, accuracy, and user confidence—fueling continuous improvement.Documentation Index
Fetch the complete documentation index at: https://docs.squared.ai/llms.txt
Use this file to discover all available pages before exploring further.
Types of Feedback Supported
1. Thumbs Up / Thumbs Down
A binary feedback option to help users indicate whether the insight was useful.- ✅ Thumbs Up — Insight was helpful
- ❌ Thumbs Down — Insight was not helpful
2. Rating (1–5 Scale)
Provides a more granular option for rating insight usefulness.- Configure number of stars (3 to 5)
- Users select one rating per insight interaction
3. Text-Based Feedback
Capture open-ended qualitative feedback from users.- Use for additional context when feedback is negative
- Example: “Prediction didn’t match actual customer churn status.”
4. Multiple Choice
Provide users with a predefined set of reasons for their rating.- Example for thumbs down:
- ❌ Not relevant
- ❌ Incomplete data
- ❌ Low confidence prediction
How to Enable Feedback
- Go to your Data App > Edit.
- Scroll to the Feedback Settings section.
- Toggle ON any of the following:
- Thumbs
- Star Ratings
- Text Input
- Multi-Select Options
- Save the Data App.
Viewing Collected Feedback
Navigate to: Reports > Data Apps Reports → Select a Data App There, you’ll find:- Feedback submission counts
- % positive feedback
- Breakdown by feedback type
- Most common comments or reasons selected
Best Practices
- Keep feedback simple and non-intrusive
- Use feedback data to validate models
- Combine with usage metrics to gauge adoption quality
Next Steps
- 👉 Monitor Usage: Analyze how your AI models are performing based on user activity and feedback.