conversational-ai-design

Conversational AI Design

Design conversational AI systems with Rasa 3.x NLU pipelines, dialogue management, and LLM-based chatbot patterns.

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Rasa NLU Pipeline

Configuration

language: en
pipeline:
  - name: WhitespaceTokenizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: DIETClassifier
    epochs: 100
  - name: EntitySynonymMapper
  - name: ResponseSelector
    epochs: 100

Intent & Entity Design

• Intents: User goals (e.g., greet, book_flight, check_status)
• Entities: Data to extract (e.g., date, location, order_id)
• Minimum 10 examples per intent for reliable classification

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Dialogue Management

Policy Stack

policies:
  - name: RulePolicy          # Handle explicit rules
  - name: TEDPolicy           # ML-based dialogue
    epochs: 100
  - name: MemoizationPolicy   # Exact conversation matches
    max_history: 5

Conversation Patterns

• Form-based: Collect structured data (bookings, orders)
• FAQ-style: Direct question → answer
• Multi-turn: Context-aware follow-ups
• Fallback: Handoff to human when confidence < threshold

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LLM Chatbot Patterns (LangChain)

Conversational Memory

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True,
    max_token_limit=2000
)

RAG for Chatbots

from langchain.chains import RetrievalQA

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vector_store.as_retriever(),
    chain_type="stuff",
    memory=memory
)

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Best Practices

• Always confirm before destructive actions
• Provide options not open-ended questions when possible
• Handle fallbacks gracefully ("I didn't understand. Try: X, Y, Z")
• Log conversations for analysis and improvement
• Test with real users — not just developers
• Set expectations — tell users what the bot can/can't do