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

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

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