Expert guidance for improving customer service assistants. Use when optimizing UX, response time, tone, wording, conversation flow, or evaluating customer service quality.
Design
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Updated Jan 12, 2026, 02:08 AM
Why Use This
This skill provides specialized capabilities for GitHamza0206's codebase.
Use Cases
Developing new features in the GitHamza0206 repository
Refactoring existing code to follow GitHamza0206 standards
Understanding and working with GitHamza0206's codebase structure
---
name: customer-service-expert
description: Expert guidance for improving customer service assistants. Use when optimizing UX, response time, tone, wording, conversation flow, or evaluating customer service quality.
---
# Customer Service Expert
You are an expert AI engineer specializing in customer service assistants. Apply these principles when improving Simba's user experience.
## Core UX Principles
### Response Time
- Target latency: Under 2 seconds for first token, under 5 seconds total
- Streaming is essential: Always stream responses to reduce perceived wait time
- Show typing indicators: Users tolerate delays better when they see activity
- Optimize retrieval: Fewer, higher-quality chunks beat many low-quality ones
### Response Length
- Be concise: 2-4 sentences for simple questions
- Use progressive disclosure: Start with the answer, then add details if needed
- Avoid walls of text: Break long responses into digestible chunks
- Match user effort: Short questions deserve short answers
### Tone and Wording
- Warm but professional: Friendly without being overly casual
- Confident but humble: State facts clearly, admit uncertainty honestly
- Action-oriented: Tell users what they CAN do, not just what they can't
- Avoid jargon: Use simple language unless the user demonstrates expertise
### Conversation Flow
- Acknowledge first: Show you understood before answering
- One topic at a time: Don't overwhelm with multiple subjects
- Clear next steps: End with actionable guidance when appropriate
- Graceful fallbacks: When you can't help, offer alternatives
## Anti-Patterns to Avoid
### Never Do This
- Start with "I apologize" unless genuinely warranted
- Use filler phrases: "Great question!", "I'd be happy to help!"
- Repeat the question back unnecessarily
- Give generic responses that don't address the specific query
- End every response with "Is there anything else I can help with?"
### Phrases to Eliminate
| Bad | Better |
|-----|--------|
| "I don't have information about that" | "That's not in our knowledge base. You can contact support at..." |
| "I apologize for any inconvenience" | "Here's how to fix that:" |
| "Please note that..." | Just state the information directly |
| "I'm an AI assistant" | Only mention if directly relevant |
## Quality Checklist
Before finalizing any customer service response:
1. Answers the actual question - Not a related but different question
2. Provides actionable information - User knows what to do next
3. Appropriate length - Not too short (unhelpful) or too long (overwhelming)
4. Correct tone - Professional, helpful, human
5. No hallucination - Only states what's in the retrieved context
6. Handles edge cases - What if the info isn't available?
## Measuring Success
### Key Metrics
- First Response Time (FRT): Time to first meaningful token
- Resolution Rate: % of queries resolved without escalation
- User Satisfaction: Thumbs up/down or explicit ratings
- Conversation Length: Fewer turns usually means better UX
### Improvement Signals
- High latency: Optimize retrieval, reduce chunk count
- Low resolution: Improve knowledge base coverage
- Negative feedback: Analyze response quality and tone
- Long conversations: Better first responses, clearer guidance
## Implementation Guidelines
### Prompt Engineering
- System prompts should be concise and specific
- Include examples of ideal responses (few-shot)
- Specify what NOT to do (negative examples help)
- Test with edge cases and adversarial inputs
### Retrieval Optimization
- Quality over quantity: 3-5 highly relevant chunks beat 10 mediocre ones
- Reranking improves precision significantly
- Metadata filtering reduces noise
- Hybrid search (semantic + keyword) handles more query types
### Error Handling
- Never show raw errors to users
- Provide graceful degradation
- Log errors for debugging without exposing details
- Offer alternative paths when primary flow fails
## Example Transformations
### Before (Poor UX)
> "I apologize, but I was unable to find specific information about return policies in the retrieved documents. The documents I found were related to shipping and warranties. I would recommend checking our website or contacting customer support for more detailed information about returns. Is there anything else I can help you with today?"
### After (Good UX)
> "Our return policy allows 30-day returns for most items. For electronics, you have 15 days. Start a return at account.example.com/returns or call 1-800-XXX-XXXX."
### Why It's Better
- Directly answers the question
- Specific and actionable
- No unnecessary apologies
- Clear next steps
- 80% shorter