Conversational AI Flow Expert

Эксперт по проектированию и реализации потоков разговорного ИИ.

Основные принципы дизайна

Управление состоянием

class ConversationState: def init(self): self.current_intent = None self.entities = {} self.conversation_history = [] self.flow_position = "start" self.confidence_threshold = 0.7

def update_context(self, user_input, intent, entities):
    self.conversation_history.append({
        "user_input": user_input,
        "intent": intent,
        "entities": entities
    })
    self.entities.update(entities)
    self.current_intent = intent

Паттерны архитектуры потоков

Маршрутизация на основе намерений

flows: booking_flow: entry_conditions: - intent: "book_appointment" steps: - name: "collect_datetime" prompt: "When would you like to schedule?" validation: "datetime_validator" - name: "confirm_booking" prompt: "Confirm booking on {datetime}?" actions: ["create_booking", "send_confirmation"]

fallback_flow: triggers: ["low_confidence", "unknown_intent"] strategy: "clarification_questions"

Паттерн заполнения слотов

def slot_filling_handler(state, required_slots): missing_slots = [s for s in required_slots if s not in state.entities]

if missing_slots:
    return generate_slot_prompt(missing_slots[0], state)

return proceed_to_next_step(state)

Обработка ошибок и восстановление

Прогрессивное раскрытие

class ErrorRecovery: def handle_misunderstanding(self, state, attempt_count): strategies = { 1: "I didn't quite catch that. Could you rephrase?", 2: "Let me try differently. Are you looking to: [options]?", 3: "Let me connect you with a human agent." } return strategies.get(attempt_count, strategies[3])

Генерация ответов

Контекстуальные шаблоны

class ResponseGenerator: templates = { "confirmation": [ "Got it! {summary}. Is that correct?", "Let me confirm: {summary}. Does this look right?" ], "progress": [ "Great! We've got {completed}. Next, {next_step}.", "Perfect! Just need {remaining} and we're done." ] }

Мультимодальные ответы

{ "response_type": "rich", "text": "Here are your options:", "components": [ { "type": "quick_replies", "options": [ {"title": "Schedule Appointment", "payload": "intent:book"}, {"title": "Check Status", "payload": "intent:status"} ] } ] }

Аналитика и оптимизация

def track_flow_metrics(conversation_id, metrics): return { "completion_rate": metrics.completed / metrics.started, "average_turns": metrics.total_turns / metrics.conversations, "fallback_rate": metrics.fallbacks / metrics.total_turns, "abandonment_points": identify_drop_off_points(conversation_id) }

Лучшие практики

• Определите четкую личность и тон бота

• Предвосхищайте потребности пользователей

• Используйте резюме для длинных диалогов

• Тестируйте все пути и edge cases

• Мониторьте реальные разговоры для улучшения