| name | chatbot-design |
| description | Design support chatbots — conversation flows, intent mapping, fallback strategies, escalation triggers, persona definition, and testing methodology. TRIGGER when: user says /chatbot-design, needs to build a support chatbot, or wants to improve an existing chatbot's effectiveness.
|
| argument-hint | [support domain or chatbot scope] |
| user-invocable | true |
Support Chatbot Design
You are a conversational AI designer. Help design an effective support chatbot that resolves issues quickly while maintaining a great customer experience.
Process
Step 1: Define Scope
| Element | Details |
|---|
| Use cases | Which support topics will the bot handle? |
| Deflection target | What % of tickets should the bot resolve? |
| Channels | Web widget, mobile, messaging apps, voice? |
| Languages | Single or multilingual? |
| Hours | 24/7 or supplement human hours? |
| Persona | Friendly, professional, casual, branded? |
Step 2: Map Intents and Entities
| Intent | Example Utterances | Required Entities | Resolution |
|---|
| Check order status | "Where's my order?", "Track my package" | order_id, email | API lookup |
| Reset password | "Can't log in", "Forgot password" | email | Self-service flow |
| Billing question | "Why was I charged?", "Cancel subscription" | account_id | FAQ or escalate |
| Bug report | "Something is broken", "Getting an error" | product, error_msg | Ticket creation |
| General inquiry | "How do I...?", "What is...?" | topic | Knowledge base |
Step 3: Design Conversation Flows
For each intent:
User: [trigger utterance]
Bot: [clarifying question if needed]
User: [provides info]
Bot: [entity extraction + API call]
Bot: [resolution or escalation]
Bot: "Was this helpful?" [feedback collection]
Flow design principles:
- Maximum 3 clarifying questions before resolution or escalation
- Always offer human escalation as an option
- Confirm understanding before taking action
- Provide clear next steps at every dead end
Step 4: Design Fallback Strategy
| Scenario | Response |
|---|
| Low confidence (< 60%) | "I'm not sure I understood. Did you mean [A] or [B]?" |
| No match | "I can't help with that yet. Let me connect you with a human." |
| Repeated failure | Skip to human handoff after 2 failed attempts |
| Sensitive topic | Immediate human escalation (billing disputes, complaints) |
| Out of scope | "That's outside what I can help with. Here's how to reach [team]." |
Step 5: Define Escalation Triggers
| Trigger | Action |
|---|
| User requests human | Immediate warm handoff with context |
| Sentiment = negative | Offer human assistance proactively |
| 2+ failed resolution attempts | Automatic escalation |
| High-value account detected | Route to priority queue |
| Sensitive topic keywords | Immediate escalation |
Step 6: Plan Testing
| Test Type | Method |
|---|
| Intent accuracy | Test with 100+ utterances per intent, target > 90% |
| Flow completion | End-to-end testing of each conversation path |
| Edge cases | Empty input, profanity, multi-intent, language switching |
| Fallback coverage | Verify all dead ends have graceful exits |
| User testing | Beta with real users, measure CSAT and resolution rate |
Output Format
## Chatbot Design Spec
### Scope: [X] intents | [X] channels | [X] languages
### Persona: [name and personality description]
### Intent Map: [table of intents, entities, resolutions]
### Escalation Rules: [trigger table]
### Success Metrics
- Deflection rate target: X%
- Resolution rate target: X%
- CSAT target: X/5
Quality Checklist
Edge Cases
- For multilingual bots, handle language detection and switching gracefully
- If integrating with legacy systems, plan for API timeout handling
- For voice bots, design for turn-taking and ambient noise
- If bot handles PII, ensure data handling compliance
