System Designer
Back to Practice Hub

Design a Conversational AI Platform

Build an intelligent conversational system that handles multi-turn dialogues with context awareness, personalization, and natural interaction flows.

System Requirements

Functional Requirements

  • Multi-turn conversation handling
  • Intent recognition and entity extraction
  • Context-aware response generation
  • Personalization and user profiling
  • Multi-modal interaction support
  • Real-time conversation analytics

Non-Functional Requirements

  • Sub-500ms response time
  • Handle 100K concurrent conversations
  • 99.9% uptime
  • 95%+ intent recognition accuracy
  • Support 50+ languages
  • GDPR and privacy compliance

Core AI Components

Natural Language Understanding (NLU)

96% accuracy50ms

Parse user input to extract intent and entities

Techniques

  • • BERT-based models
  • • Named entity recognition
  • • Sentiment analysis

Challenges

  • • Ambiguous queries
  • • Context dependency
  • • Multi-intent detection

Performance

Accuracy: 96%
Latency: 50ms

Dialogue Management

92% accuracy20ms

Track conversation state and decide next actions

Techniques

  • • State machines
  • • Graph-based flows
  • • Reinforcement learning

Challenges

  • • Context switching
  • • Error recovery
  • • User goal modeling

Performance

Accuracy: 92%
Latency: 20ms

Natural Language Generation (NLG)

89% accuracy200ms

Generate contextual and personalized responses

Techniques

  • • Template-based
  • • Neural generation
  • • Retrieval-augmented

Challenges

  • • Consistency
  • • Personality matching
  • • Avoiding repetition

Performance

Accuracy: 89%
Latency: 200ms

System Architecture

User Input → Input Processing → NLU Service
↓ ← Context Store (Redis)
Intent & Entities → Dialogue Manager → Policy Engine
↓ ← Knowledge Base (RAG System)
Action Selection → NLG Service → Response Post-processing
↓
Response Delivery → User Interface → Analytics Pipeline

Input Processing

  • Speech-to-text
  • Text normalization
  • Language detection
  • Input validation

Understanding

  • Intent classification
  • Entity extraction
  • Context parsing
  • Sentiment analysis

Dialogue Management

  • State tracking
  • Policy learning
  • Action selection
  • Context management

Response Generation

  • Template selection
  • Neural generation
  • Personalization
  • Output formatting

Capacity Estimation

Conversation Patterns & Load

Conversation Types
60%Simple
40%Complex
Session Duration
200sShort
1800sExtended
Processing Load
30%NLU
70%Generation

Performance Metrics

Daily Conversations
Peak: 50K concurrent
10M+
Response Time P95
SLA: < 500ms
450ms
Intent Accuracy
Multi-domain NLU
96%
User Satisfaction
Feedback-driven optimization
4.2/5

Infrastructure Requirements

API Gateway
500+ servers for 50K concurrent
AI Models
200 GPU servers
Session Store
50TB Redis cluster

Advanced Conversational Features

Multi-turn Context Management

Context Tracking Strategies:
  • • Conversation history sliding window
  • • Entity and slot value persistence
  • • Intent stack for nested conversations
  • • User preference and profile memory

Personalization Engine

Adaptive Response Generation:
  • • User communication style matching
  • • Topic preference learning
  • • Conversation flow optimization
  • • Emotional intelligence adaptation

Error Handling

• Graceful fallback responses
• Clarification question generation
• Context repair strategies
• Human handoff triggers

Multi-modal Support

• Voice and text integration
• Image understanding
• Rich media responses
• Cross-channel consistency

Analytics & Learning

• Conversation flow analysis
• Success metric tracking
• A/B testing framework
• Continuous model improvement

Database Schema

conversations

conversation_id (UUID, Primary Key) user_id channel (web/mobile/voice) started_at last_activity status (active/completed/abandoned) context (JSON) metadata (JSON) session_ttl

messages

message_id (UUID, Primary Key) conversation_id (Foreign Key) sender (user/assistant) content intent entities (JSON) confidence_score timestamp response_time

user_profiles

user_id (Primary Key) preferences (JSON) conversation_style topics_of_interest (array) language timezone created_at last_interaction interaction_count

intents

intent_id (Primary Key) name description training_examples (JSON) entities (array) response_templates (JSON) confidence_threshold active_status

Practice Questions

1

How would you design context management for multi-turn conversations that can handle topic switches and interruptions?

2

Design a personalization system that adapts conversation style and content based on user behavior and preferences.

3

How do you handle ambiguous queries and implement clarification strategies that feel natural to users?

4

Implement error recovery and graceful degradation when AI components fail or produce low-confidence results.

5

Design a multi-modal conversation system that seamlessly integrates voice, text, and visual inputs/outputs.