Collect Better Human Feedback on Your AI Outputs
Add a single HTML attribute to start capturing user reactions
60-Second Integration
<!-- Add to your <head> -->
<script src="https://cdn.coolhand.ai/coolhand.min.js"></script>
<!-- Wrap any AI output -->
<div coolhand-feedback>
AI response content...
</div>
Add the script, then mark any element with coolhand-feedback to enable rating and revision tracking. Works with any AI provider.
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Adjust Email Tone Based on User Feedback
Adjust Email Tone Based on User Feedback
Summary
Analysis of 52 user revisions revealed that users consistently edited overly formal email drafts to sound more natural and direct. Update the system prompt to produce warmer, more conversational emails.
Feedback Pattern Detected
The "Email Drafter" workload showed a 38% revision rate with consistent patterns:
- • Users replaced "I hope this email finds you well" with direct openers
- • Users changed formal phrases like "Please do not hesitate to contact me" to "Let me know"
- • Users shortened sign-offs from "Best regards" to "Best" or "Thanks"
Before
system: "You are a professional email assistant.
Draft polished, professional emails
for business communication."After
system: "You are an email assistant.
Draft emails that are warm but concise.
Skip formal pleasantries. Get to the point.
Use casual sign-offs like 'Best' or 'Thanks'."Expected Impact
| Metric | Before | After |
|---|---|---|
| Revision rate | 38% | ~12% |
| Avg. email length | ~180 words | ~120 words |
| Output tokens | ~240 | ~160 |
| User satisfaction | 62% positive | ~88% positive |
Low Risk
Prompt-only change. Easy to revert. Recommended: A/B test with 20% of traffic before full rollout.
Your AI Moat Is Human Insight + Rapid Iteration
The model isn't your advantage—how fast you learn from users is. Coolhand closes the feedback loop automatically with PRs and issues.
Monitor
Coolhand tracks your AI requests and user feedback in real-time, building a complete picture of your workloads.
Analyze
AI identifies patterns, optimization opportunities, and issues from your data, backed by latest LLM research.
Automate
Get PRs for prompt fixes, issues for bigger changes—all with risk labels and research-backed explanations.
Simple Fixes
Automatic PR submission for prompt updates and validation rules
Complex Changes
Detailed issues with suggested approaches for architectural improvements
Risk Assessment
All recommendations include risk labels and sandbox-tested solutions