Designing a Robust Chatbot: Handling a Variety of Query Types

1. Small Talk: Greetings & Farewells

Chatbots should be personable and able to engage in basic social interactions. This helps create a welcoming user experience.

Examples:

• "Good morning! How are you?"
• "Hello, are you there?"
• "Thanks for your help, goodbye!"
• "Have a nice day!"
• "See you later!"

2. Gibberish

Users may input nonsense or random characters. The chatbot should gracefully handle such inputs without getting confused.

Examples:

• "asdflkjweroi"
• "blah blah blah"
• "12345abcde"
• "!!!###@@@"
• "wqeoipouqewp"

3. Follow-Up Queries Within Context

Users often ask follow-up questions that directly relate to the previous conversation.

Examples:

• "What is my remaining leave balance?"
  • "Can I carry over the unused leaves?"
• "How do I reset my password?"
  • "How long will it take?"
• "What is the process for claiming medical reimbursement?"
  • "Can I claim for last year?"

4. Follow-Up Queries Outside Context

Users might ask follow-up questions that are unrelated to the current conversation.

Examples:

• "What are my benefits?"
  • "What's the weather like today?"
• "How do I submit a timesheet?"
  • "What's the CEO's name?"

5. Queries with Spelling Mistakes

Users may make typographical errors, and the chatbot should still understand and respond correctly.

Examples:

• "Whar is my leav balnce?"
• "How do I aply for medcal insurence?"
• "Wen is the next company evnt?"
• "Plase help with my passwrd."
• "How do I clame travel reimbusment?"

6. Form-Filling Guided Flows

Some interactions require collecting multiple pieces of information. The chatbot should guide the user through this process smoothly.

Examples:

• "I need to update my address." → [Guided flow: asks for current address, new address, and effective date]
• "I want to apply for leave." → [Guided flow: asks for leave type, dates, and reason]
• "How do I submit an expense report?" → [Guided flow: asks for expense type, amount, and date]

7. Out of Scope Queries

Not all queries will fall within the chatbot's scope. It should handle such situations gracefully.

Examples:

• "Can you tell me who won the football game last night?"
• "What's the best place to buy a laptop?"
• "How do I cook lasagna?"
• "What is the meaning of life?"
• "Can you book me a flight to Paris?"

8. Queries with Multiple Intents (Dependent)

Sometimes users ask questions that have multiple, dependent parts that must be resolved in sequence.

Examples:

• "Can you check my leave balance and then help me apply for leave?"
• "Tell me my health benefits and then explain how to claim them."
• "What's my current salary, and can you show me my last payslip?"

9. Queries with Multiple Intents (Independent)

Users may combine multiple, unrelated queries that can be addressed simultaneously.

Examples:

• "What's my leave balance and who is my HR manager?"
• "Show me my payslip and update my contact information."
• "How do I apply for a promotion and what are the insurance options?"

10. Inappropriate or Offensive Content

The chatbot should identify and respond appropriately to any inappropriate or offensive content.

Examples:

• "You're useless, just like this company!"
• "Can you help me with something illegal?"
• "This job sucks, and so do you."

11. Queries with Incomplete Information

Sometimes users provide incomplete data, and the chatbot needs to ask for clarification or additional details.

Examples:

• "I need to update my address." (Missing new address)
• "Help me apply for leave." (Missing leave dates)
• "Show me my payslip." (Missing month)

12. Escalation Queries

Users may request to speak with a human or escalate an issue that the chatbot cannot resolve.

Examples:

• "I need to speak with HR."
• "Can you connect me to a manager?"
• "This isn't helping, can I talk to a person?"
• "I need to escalate this issue."

13. Queries About the Bot's Capabilities

Users might ask what the chatbot can or cannot do.

Examples:

• "What can you help me with?"
• "Are you able to book meetings?"
• "Can you explain company policies?"
• "How do you handle personal data?"

14. Feedback Queries

Users might want to provide feedback on their experience with the chatbot.

Examples:

• "You're doing a great job!"
• "This wasn't helpful at all."
• "Can I leave feedback?"
• "I'd like to suggest an improvement."

15. Prompts Containing PII (Personal Identifiable Information)

Users may inadvertently share sensitive information. The chatbot should handle this with care.

Examples:

• "My Social Security Number is 123-45-6789."
• "Here's my credit card number: 4111 1111 1111 1111."
• "My address is 123 Main St, Springfield."

16. Clarification Queries

Users might ask for clarification on information provided by the chatbot. The chatbot should be able to restate or elaborate on its responses.

Examples:

• "What do you mean by 'pro-rata basis'?"
• "Can you explain what 'carryover leave' is?"
• "How does the reimbursement process work again?"
• "Can you give me an example of how to fill out the expense form?"

17. Emergency Queries

In rare cases, users might need urgent help. The chatbot should be prepared to respond appropriately and escalate as needed.

Examples:

• "There's been an accident at work, what do I do?"
• "I need to report an emergency."
• "Who do I contact in case of a workplace incident?"
• "There's a fire alarm, what's the procedure?"

Conclusion

Equipping your chatbot to handle these diverse types of queries ensures it can engage effectively with users, handle various scenarios, and maintain a high level of service. Each query type requires thoughtful design and development to ensure the chatbot responds appropriately and maintains user trust.

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