Response Feedback

Collect structured feedback on AI-generated responses with ratings and reasons.

228|18|Updated Dec 7, 2024

Search Tags:#chatbot #user feedback #regeneration #ai improvement #ai feedback #response rating

Ready to run

npx skills add https://github.com/thedaviddias/ux-patterns-for-developers --skill response-feedback

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Response Feedback
Source: https://github.com/thedaviddias/ux-patterns-for-developers/tree/main/skills/response-feedback
Command: npx skills add https://github.com/thedaviddias/ux-patterns-for-developers --skill response-feedback

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured way to collect user feedback on AI-generated responses, enabling continuous improvement of AI models and user experience.

Core Features & Use Cases

Rate AI Responses: Users can quickly provide thumbs up/down feedback on AI outputs.
Capture Reasons: Optionally, users can select or provide specific reasons for their feedback.
Regeneration: Allows users to request a new response if the current one is unsatisfactory.
Use Case: In a customer support chatbot, after the AI provides an answer, this Skill allows the user to rate the answer's helpfulness and trigger a regeneration if needed, helping to refine the AI's knowledge base.

Quick Start

Use the response feedback skill to rate the last AI response as helpful.

Frequently Asked Questions about Response Feedback

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I collect user feedback on AI chatbot responses?▼

To collect user feedback on AI chatbot responses, implement rating mechanisms like thumbs up or thumbs down, optionally capturing specific reasons to evaluate quality and guide improvements.

Can I trigger response regeneration for unsatisfactory AI answers?▼

Yes, response regeneration for unsatisfactory AI answers is supported, allowing users to request a new response while preserving context for iterative improvement.

What is the best way to capture reasons for negative AI response ratings?▼

The best way to capture reasons for negative AI response ratings is to optionally prompt users to select or provide specific reasons alongside their thumbs down feedback.

Does this response feedback mechanism preserve context during regeneration loops?▼

Yes, the response feedback mechanism preserves context during regeneration loops, maintaining the original conversation state to generate improved iterative answers.

How does rating AI outputs help with copilot quality review?▼

Rating AI outputs helps with copilot quality review by providing structured feedback data that identifies unsatisfactory answers and refines the AI knowledge base.