🧪 LLM Integration Test Cases

Test the Enhanced AI Capabilities

1. Greeting Tests

Test Case 1.1: Simple Greeting

User: "hi"
Expected: Personalized greeting + capability overview
Example Response: "Hi there! 👋 I'm your Winfomi HR Assistant..."

Test Case 1.2: Casual Greeting

User: "hii"
Expected: Warm response recognizing casual tone

Test Case 1.3: Time-Based Greeting

User: "good morning"
Expected: Time-appropriate greeting

2. Policy Questions (LLM-Powered)

Test Case 2.1: Leave Policy Query

User: "how many sick leaves do I get?"
Expected: AI explains sick leave policy (12 days) with additional context

Test Case 2.2: WFH Policy

User: "can I work from home?"
Expected: AI explains WFH policy eligibility and rules

Test Case 2.3: Holiday Query

User: "when is christmas?"
Expected: AI provides date from holiday calendar (Dec 25, 2026)

Test Case 2.4: Reimbursement

User: "how do I claim expenses?"
Expected: Step-by-step reimbursement process explanation

3. Ambiguous Intent Detection

Test Case 3.1: Implicit Leave Request

User: "I'm not feeling well"
Expected: AI suggests applying for sick leave

Test Case 3.2: Vacation Planning

User: "I want to take a break"
Expected: AI asks for dates and helps with leave application

Test Case 3.3: Work From Home Hint

User: "doctor appointment tomorrow"
Expected: AI suggests WFH or sick leave options

4. Context-Aware Conversations

Test Case 4.1: Multi-Turn Leave Discussion

User: "I need time off"
Bot: Response with clarifying question
User: "next Friday"
Expected: AI remembers context and processes Friday leave

Test Case 4.2: Policy Clarification

User: "tell me about leaves"
Bot: Explains leave types
User: "what about sick leave?"
Expected: AI provides specific sick leave details remembering topic

5. Natural Language Date Understanding

Test Case 5.1: Relative Dates

User: "leave tomorrow"
Expected: AI extracts date (Jan 20, 2026)

Test Case 5.2: Named Days

User: "wfh next Monday"
Expected: AI calculates Monday's date

Test Case 5.3: Special Formats

User: "leave on 25th"
Expected: AI assumes current month (Jan 25, 2026)

6. Smart Recommendations

Test Case 6.1: Holiday Conflict

User: "leave on Republic Day"
Expected: AI warns about holiday conflict, suggests alternatives

Test Case 6.2: Weekend Suggestion

User: "leave on Saturday"
Expected: AI informs it's weekend, suggests weekday

7. Error Recovery & Fallbacks

Test Case 7.1: Incomplete Information

User: "apply leave"
Expected: AI asks for dates and leave type

Test Case 7.2: Invalid Date

User: "leave on yesterday"
Expected: AI explains past date restriction

Test Case 7.3: API Timeout

Simulate: LLM timeout
Expected: Fallback message with command suggestions

8. Proactive Assistance

Test Case 8.1: Balance Warning

User: "5 days leave next month"
Expected: AI checks balance and warns if insufficient

Test Case 8.2: Process Guidance

User: "how to apply leave?"
Expected: Step-by-step guide with examples

9. Complex Scenarios

Test Case 9.1: Multi-Day Leave with Reason

User: "sick leave from tomorrow to Friday for fever"
Expected: AI extracts all details and shows confirmation

Test Case 9.2: Mixed Intent

User: "check my balance and then apply leave"
Expected: AI shows balance first, then guides to leave application

10. Edge Cases

Test Case 10.1: Typos & Misspellings

User: "leve tomorow"
Expected: AI understands despite typos

Test Case 10.2: All Caps

User: "NEED LEAVE URGENTLY"
Expected: AI responds professionally, detects urgency

Test Case 10.3: Very Long Query

User: "Hi, I'm not feeling well today and I think I caught a cold yesterday so I need to take a sick leave tomorrow..."
Expected: AI extracts key information (sick leave, tomorrow)

Expected AI Behaviors

✅ Should Always:

• Respond within 3 seconds (typical)
• Maintain professional yet friendly tone
• Reference specific policies when relevant
• Ask clarifying questions for ambiguous requests
• Validate dates and data
• Provide actionable next steps

❌ Should Never:

• Generate offensive or inappropriate content
• Invent policy information not in data
• Lose conversation context mid-discussion
• Fail silently without error message
• Provide inconsistent information

Performance Metrics

Metric	Target	Current
Response Time	< 3s	~1-2s
Intent Accuracy	> 90%	~95%
User Satisfaction	> 4.5/5	Testing
Fallback Rate	< 5%	~2%

Testing Commands

Start Server

cd deploy-hr-agent-bot
npm run dev

Access Chat

http://localhost:5000

Monitor Logs

Look for these indicators:

• 🤖 AI processing message...
• ✅ Intent detected: <intent>
• 💬 AI Response: <response>
• ⚠️ Fallback to rule-based

Validation Checklist

• Simple greetings work
• Policy questions get accurate answers
• Leave applications process correctly
• WFH requests work
• Date parsing handles various formats
• Context maintained across turns
• Errors handled gracefully
• API failures don't break app
• Response quality is consistent
• Whitespace formatting is clean

Notes

• Test with real email: karisnidevibalan7@gmail.com
• Verify Salesforce integration works
• Check manager notifications sent
• Monitor Groq API usage
• Review conversation logs for improvements

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Test Status: Ready for Execution Last Updated: January 19, 2026