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Design a Conversational AI Platform

Build an enterprise-grade conversational AI platform supporting multi-modal interactions, 100K concurrent conversations, and multi-tenant architecture.

NLU/NLGDialog SystemsMulti-modal AI
Q: What type of conversational AI platform are we building - customer service, virtual assistant, or something else?
A: A multi-purpose platform supporting customer service, virtual assistants, and task automation. Think enterprise-grade solution like Google Dialogflow or Amazon Lex.
Engineering Implications: This requires a flexible architecture supporting multiple use cases, custom integrations, and different conversation patterns (transactional, informational, task-oriented).
Q: What's the expected scale in terms of users and conversations?
A: 100K concurrent conversations, 10M daily active users, supporting 1000+ enterprise customers with multi-tenant architecture.
Engineering Implications: Massive scale requires distributed processing, efficient session management, and tenant isolation. We'll need horizontal scaling with proper load balancing.
Q: What channels and modalities need to be supported?
A: Text (web chat, SMS, messaging apps), voice (phone, smart speakers), and rich media (images, documents, videos). API-first for custom integrations.
Engineering Implications: Multi-modal processing requires different pipelines for each modality, unified context management across channels, and media processing capabilities.
Q: What are the latency and accuracy requirements?
A: Sub-500ms response time for text, {'<'}2s for voice including TTS/STT. Intent recognition accuracy >95%, task completion rate >85%.
Engineering Implications: Low latency requires optimized inference, caching strategies, and potentially edge deployment. High accuracy needs robust NLU models and continuous learning.
Q: What about personalization and context management?
A: Full conversation history, user preferences, cross-session context, and integration with CRM/enterprise systems for personalization.
Engineering Implications: Requires persistent storage, user profiling systems, and secure data handling. Context window management becomes critical for long conversations.
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Interview Practice Questions

Practice these open-ended questions to prepare for system design interviews. Think through each scenario and discuss trade-offs.

1

Multi-turn Context Management: Design a conversation system that can handle complex multi-turn dialogues where users change topics mid-conversation, refer to previous context, and ask follow-up questions. How do you maintain context relevance while preventing context window explosion in long conversations?

2

Voice-First Design: Your conversational AI needs to work primarily through voice interfaces (Alexa, phone calls, smart speakers). How do you design natural voice interactions, handle speech recognition errors, and create audio-first user experiences without visual cues?

3

Enterprise Integration: Design a conversational AI that integrates with enterprise systems (CRM, ticketing, databases) to provide personalized assistance. Handle authentication, data access permissions, and real-time system queries while maintaining conversation flow and security.

4

Personalization at Scale: Build a conversation system that learns from individual user interactions to provide personalized responses and suggestions. How do you balance personalization with privacy, handle cold start problems, and prevent overfitting to individual user quirks?

5

Multilingual Conversations: Design a system that can seamlessly handle conversations that switch languages mid-dialogue, including different writing systems (Latin, Chinese, Arabic) and cultural communication patterns. Address both technical (encoding, models) and product (user experience) challenges.

6

Error Recovery and Learning: When your conversational AI misunderstands user intent or provides wrong information, design a system that can gracefully recover, learn from mistakes, and improve over time. Include both immediate conversation repair and long-term system improvement mechanisms.