Chatbot Design
Framework
IRON LAW: Intent First, Response Second
A chatbot must UNDERSTAND what the user wants (intent) before crafting
a response. Building response templates without intent classification
produces a keyword-matching FAQ, not a chatbot.
Flow: User message → Intent classification → Slot extraction → Response
Core NLU Pipeline
| Stage |
What It Does |
Example |
| Intent Classification |
Identify what the user wants to do |
"What time do you close?" → intent: check_hours |
| Entity/Slot Extraction |
Extract key information from the message |
"Book a table for 4 on Friday" → slots: {party_size: 4, date: Friday} |
| Dialogue Management |
Decide the next action (ask for missing info, confirm, execute) |
Missing slot time → ask "What time would you like?" |
| Response Generation |
Produce the reply |
"I've booked a table for 4 on Friday at 7pm. See you then!" |
Intent Design
- Start with 10-15 core intents covering 80% of user queries
- Each intent needs 10-20 training examples (varied phrasings)
- Include a
fallback intent for unrecognized inputs
- Group related intents:
order_status, order_cancel, order_modify under "Order Management"
Dialogue Flow Patterns
| Pattern |
When to Use |
Example |
| Single-turn |
Simple Q&A, no context needed |
"What are your hours?" → respond immediately |
| Multi-turn (slot filling) |
Need multiple pieces of info |
"Book a table" → ask party size → ask date → ask time → confirm |
| Branching |
Different paths based on user's answer |
"Do you have an account?" → Yes: login flow / No: registration flow |
| Confirmation |
Before executing actions |
"I'll cancel order #12345. Is that correct?" |
| Handoff |
Bot can't handle the request |
"Let me connect you with a human agent" |
Response Design Principles
- Acknowledge first: "Got it, you want to check your order status."
- Be concise: Answer the question, then stop. Don't add unnecessary information.
- Offer next steps: "Is there anything else I can help with?" or suggest related actions.
- Use quick replies/buttons: Reduce typing, guide the conversation.
- Personality: Define a consistent tone (friendly, professional, casual) and stick to it.
Metrics
| Metric |
Definition |
Target |
| Intent accuracy |
% correctly classified intents |
> 85% |
| Containment rate |
% resolved without human handoff |
> 60-70% |
| CSAT |
Customer satisfaction score |
> 4.0/5 |
| Fallback rate |
% triggering fallback/unknown intent |
< 15% |
| Resolution time |
Average time to resolve |
< 2 minutes |
Output Format
# Chatbot Design: {Use Case}
## Intent Catalog
| Intent | Description | Example Utterances | Priority |
|--------|-----------|-------------------|---------|
| {intent} | {what it means} | "{example 1}", "{example 2}" | H/M/L |
## Dialogue Flows
### {Flow Name}
1. User: {trigger utterance}
2. Bot: {response + slot question if needed}
3. User: {provides info}
4. Bot: {confirmation or action}
## Fallback Strategy
- After 1 miss: rephrase + suggest options
- After 2 misses: offer human handoff
## Metrics Targets
| Metric | Target |
|--------|--------|
| Intent accuracy | > {X%} |
| Containment | > {X%} |
Gotchas
- Users don't follow your flow: People type in unexpected ways, change topics mid-conversation, and give incomplete information. Design for messiness, not just the happy path.
- Fallback is your most important intent: A good fallback ("I'm not sure I understood. Did you mean X, Y, or Z?") is better than a bad guess.
- LLM-powered bots still need guardrails: Using GPT/Claude for response generation? Add intent classification as a first layer to route and constrain, preventing hallucination and off-topic responses.
- Test with real users, not team members: Your team knows how the bot works and phrases things "correctly." Real users don't. Test with 10+ real users before launch.
- Conversation logs are gold: Review conversation logs weekly. Failed conversations reveal missing intents, confusing flows, and training data gaps.
References
- For NLU training data best practices, see
references/nlu-training.md
- For LINE/Messenger platform integration, see the ecom-conversational skill
1---2name: cs-chatbot-design3description: Design conversational AI chatbots including intent recognition, slot filling, dialogue flow, and response generation. Use this skill when the user needs to build a chatbot, design conversation flows, implement intent classification, or improve chatbot accuracy — even if they say 'build a chatbot', 'our bot doesn't understand users', 'design a FAQ bot', or 'improve our chatbot's responses'.4---56# Chatbot Design78## Framework910```11IRON LAW: Intent First, Response Second1213A chatbot must UNDERSTAND what the user wants (intent) before crafting14a response. Building response templates without intent classification15produces a keyword-matching FAQ, not a chatbot.1617Flow: User message → Intent classification → Slot extraction → Response18```1920### Core NLU Pipeline2122| Stage | What It Does | Example |23|-------|-------------|---------|24| **Intent Classification** | Identify what the user wants to do | "What time do you close?" → intent: `check_hours` |25| **Entity/Slot Extraction** | Extract key information from the message | "Book a table for 4 on Friday" → slots: {party_size: 4, date: Friday} |26| **Dialogue Management** | Decide the next action (ask for missing info, confirm, execute) | Missing slot `time` → ask "What time would you like?" |27| **Response Generation** | Produce the reply | "I've booked a table for 4 on Friday at 7pm. See you then!" |2829### Intent Design3031- **Start with 10-15 core intents** covering 80% of user queries32- Each intent needs 10-20 training examples (varied phrasings)33- Include a `fallback` intent for unrecognized inputs34- Group related intents: `order_status`, `order_cancel`, `order_modify` under "Order Management"3536### Dialogue Flow Patterns3738| Pattern | When to Use | Example |39|---------|-----------|---------|40| **Single-turn** | Simple Q&A, no context needed | "What are your hours?" → respond immediately |41| **Multi-turn (slot filling)** | Need multiple pieces of info | "Book a table" → ask party size → ask date → ask time → confirm |42| **Branching** | Different paths based on user's answer | "Do you have an account?" → Yes: login flow / No: registration flow |43| **Confirmation** | Before executing actions | "I'll cancel order #12345. Is that correct?" |44| **Handoff** | Bot can't handle the request | "Let me connect you with a human agent" |4546### Response Design Principles47481. **Acknowledge first**: "Got it, you want to check your order status."492. **Be concise**: Answer the question, then stop. Don't add unnecessary information.503. **Offer next steps**: "Is there anything else I can help with?" or suggest related actions.514. **Use quick replies/buttons**: Reduce typing, guide the conversation.525. **Personality**: Define a consistent tone (friendly, professional, casual) and stick to it.5354### Metrics5556| Metric | Definition | Target |57|--------|-----------|--------|58| **Intent accuracy** | % correctly classified intents | > 85% |59| **Containment rate** | % resolved without human handoff | > 60-70% |60| **CSAT** | Customer satisfaction score | > 4.0/5 |61| **Fallback rate** | % triggering fallback/unknown intent | < 15% |62| **Resolution time** | Average time to resolve | < 2 minutes |6364## Output Format6566```markdown67# Chatbot Design: {Use Case}6869## Intent Catalog70| Intent | Description | Example Utterances | Priority |71|--------|-----------|-------------------|---------|72| {intent} | {what it means} | "{example 1}", "{example 2}" | H/M/L |7374## Dialogue Flows75### {Flow Name}761. User: {trigger utterance}772. Bot: {response + slot question if needed}783. User: {provides info}794. Bot: {confirmation or action}8081## Fallback Strategy82- After 1 miss: rephrase + suggest options83- After 2 misses: offer human handoff8485## Metrics Targets86| Metric | Target |87|--------|--------|88| Intent accuracy | > {X%} |89| Containment | > {X%} |90```9192## Gotchas9394- **Users don't follow your flow**: People type in unexpected ways, change topics mid-conversation, and give incomplete information. Design for messiness, not just the happy path.95- **Fallback is your most important intent**: A good fallback ("I'm not sure I understood. Did you mean X, Y, or Z?") is better than a bad guess.96- **LLM-powered bots still need guardrails**: Using GPT/Claude for response generation? Add intent classification as a first layer to route and constrain, preventing hallucination and off-topic responses.97- **Test with real users, not team members**: Your team knows how the bot works and phrases things "correctly." Real users don't. Test with 10+ real users before launch.98- **Conversation logs are gold**: Review conversation logs weekly. Failed conversations reveal missing intents, confusing flows, and training data gaps.99100## References101102- For NLU training data best practices, see `references/nlu-training.md`103- For LINE/Messenger platform integration, see the ecom-conversational skill
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