11. Unit Testing
2   - Individual components
3   - Single intents/flows
4 
52. Integration Testing
6   - Connected systems
7   - API interactions
8 
93. End-to-End Testing
10   - Full conversations
11   - Complete scenarios
12 
134. User Acceptance Testing
14   - Real users
15   - Real scenarios

1Cover all paths:
2□ Happy paths (everything works)
3□ Edge cases (unusual inputs)
4□ Error scenarios (things break)
5□ Boundary conditions (limits)
6□ Security cases (abuse attempts)

1For each intent, test:
2- Standard request
3- Variations in wording
4- Missing information
5- Extra information
6- Corrections mid-flow
7- Abandonment and return

1Intent: order_tracking
2 
3Test 1: Standard
4"Track my order #12345"
5Expected: Show order status
6 
7Test 2: No order number
8"Where's my order?"
9Expected: Ask for order number
10 
11Test 3: Invalid format
12"Track order ABC"
13Expected: Ask for valid number
14 
15Test 4: Order not found
16"Track order #99999"
17Expected: Graceful not found message
18 
19Test 5: With context
20User just provided email
21"Track my recent order"
22Expected: Use email to find order

1Build test suite:
21. Create test cases file
32. Input → Expected output
43. Run automatically
54. Compare results
65. Report failures
7 
8Tools:
9- Platform built-in testing
10- Custom scripts
11- CI/CD integration

1Volume metrics:
2- Total conversations
3- Messages per conversation
4- Peak hours
5- Growth trends
6 
7Performance metrics:
8- Resolution rate
9- Handoff rate
10- First response time
11- Time to resolution
12 
13Quality metrics:
14- Intent accuracy
15- User satisfaction
16- Task completion rate
17- Return rate

1Track per intent:
2- Volume
3- Success rate
4- Average turns to resolve
5- Common follow-up intents
6- Failure reasons

1Where do users abandon?
21. Track each step
32. Identify high drop-off points
43. Analyze why
54. Improve those flows
6 
7Example:
8Welcome → 95% continue
9Ask email → 70% continue ← Problem!
10Verify → 90% continue
11 
12Action: Simplify email collection

1After conversation:
2"Was I helpful today? 👍 👎"
3 
4On negative:
5"I'm sorry! What could I have done better?"
6 
7Options:
8- Thumbs up/down
9- 1-5 star rating
10- Text feedback
11- Specific questions

1Aggregate:
2- Overall satisfaction score
3- Score by intent
4- Score by time period
5- Common complaints
6 
7Text analysis:
8- Sentiment
9- Keywords
10- Themes
11- Specific issues

11. Analyze
2   - Review metrics
3   - Read conversations
4   - Check feedback
5 
62. Identify Issues
7   - Low performing flows
8   - Common failures
9   - User complaints
10 
113. Hypothesize
12   - Why is it failing?
13   - What would fix it?
14 
154. Implement
16   - Update prompts
17   - Add training data
18   - Improve flows
19 
205. Measure
21   - A/B test if possible
22   - Track improvement
23 
246. Repeat

1Improve AI responses:
2 
3Original prompt:
4"Answer the user's question."
5 
6Better prompt:
7"You are a helpful customer service agent.
8Answer the user's question concisely.
9If you don't know, say so.
10Always offer additional help."
11 
12Test both, measure quality difference.

1Simplify flows:
2- Reduce steps
3- Combine questions
4- Add shortcuts
5- Remove unnecessary
6 
7Before: 5 steps to track order
8After: 2 steps (ask number, show status)

1Test variations:
2- Welcome messages
3- Button labels
4- Response wording
5- Flow order
6- Prompt templates
7- Personality tone

1Split traffic:
2- 50% see version A
3- 50% see version B
4- Same time period
5- Sufficient sample size
6 
7Track:
8- Completion rate
9- Satisfaction
10- Time to complete
11- Any key metric

1Statistical significance:
2- Need enough data
3- Calculate confidence
4- Don't conclude too early
5 
6Example:
7Version A: 72% success (500 users)
8Version B: 78% success (500 users)
9Confidence: 95%
10Winner: Version B

1Optimize for speed:
2- Minimize API calls
3- Cache common responses
4- Parallel processing
5- Efficient prompts

1Reduce AI costs:
2- Use smaller models where possible
3- Limit context length
4- Cache responses
5- Batch requests

1Handle growth:
2- Monitor usage
3- Plan for peak loads
4- Auto-scaling
5- Load balancing

1Schedule:
2- Daily: Quick scan for issues
3- Weekly: Deep dive into failures
4- Monthly: Full analysis
5 
6Review:
7- Failed conversations
8- Low-rated interactions
9- Handoff conversations
10- Long conversations

1Common issues:
2- Misunderstood intents
3- Missing information
4- Loop conversations
5- Abrupt endings
6- Frustrated users
7- Unanswered questions

1From reviews:
21. Add training phrases
32. Update prompts
43. Add edge case handling
54. Create new intents
65. Improve error messages
76. Add fallback paths

1Key visualizations:
2- Conversation volume over time
3- Resolution rate trend
4- Top intents (pie chart)
5- Satisfaction score
6- Handoff reasons
7- Error frequency

1Monitor:
2- Current active conversations
3- Error rate (last hour)
4- Response time (current)
5- API health status
6- Queue depth (if using)

1Before optimization:
2- Resolution rate: 65%
3- Satisfaction: 3.5/5
4- Avg conversation length: 8 turns
5- Handoff rate: 25%
6 
7Target:
8- Resolution rate: 80%+
9- Satisfaction: 4.0/5+
10- Avg conversation length: 5 turns
11- Handoff rate: <15%

1Weekly tracking:
2- Compare to baseline
3- Note improvements
4- Document changes
5- Celebrate wins!

1Useful tools:
2- Postman (API testing)
3- Custom scripts
4- Spreadsheet tracking
5- Analytics platforms