Chatbot Design 聊天機器人設計

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industry customer-service

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'.

客服技能：Chatbot Design 分析與應用。

View on GitHub在 GitHub 查看

Framework 框架

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輸出格式

# Chatbot Design: {Use Case}

Gotchas注意事項

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參考資料

For NLU training data best practices, see references/nlu-training.md
For LINE/Messenger platform integration, see the ecom-conversational skill

Tags標籤

chatbotconversational-ainludialogue