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

check_hours

time

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

Response Design Principles

1. Acknowledge first: "Got it, you want to check your order status."
2. Be concise: Answer the question, then stop. Don't add unnecessary information.
3. Offer next steps: "Is there anything else I can help with?" or suggest related actions.
4. Use quick replies/buttons: Reduce typing, guide the conversation.
5. Personality: Define a consistent tone (friendly, professional, casual) and stick to it.

Metrics

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