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921Understanding Stateless Architecture in LLM Applications
922Understanding Stateful Architecture in LLM Applications
923Trade-offs: Scalability and Simplicity
924Client-Side State Management
925Server-Side Session Storage
926Session Affinity and Load Balancing
927State Serialization and Token Limits
928Hybrid Architectures: Best of Both Worlds
929Session Expiration and Cleanup
930When to Choose Stateless vs Stateful
931Synchronous Request-Response Basics
932When to Use Synchronous Patterns
933Asynchronous Pattern Fundamentals
934Task Queues for LLM Workloads
935WebSockets for Real-Time Streaming
936Webhook-Based Completion Notifications
937Polling Patterns and Best Practices
938Background Processing with Workers
939Async/Await in Python for Concurrent Requests
940Timeout and Cancellation Handling
941User Experience Trade-offs
942Hybrid Patterns for Complex Workflows
943Choosing the Right Database for LLM Applications
944Session Storage for Conversational State
945Document Storage for User Data and Context
946Metadata and Application State Management
947Vector Database Integration Patterns
948Message Queues and Event Streaming
949Blob Storage for Large Context and Artifacts
950Database Sharding and Partitioning Strategies
951Transactional Consistency in AI Workflows
952Storage Cost Optimization and Data Lifecycle
953Why Caching Matters for LLM Applications
954Semantic vs Exact Caching
955Cache Key Design for Prompts
956In-Memory Caching with Redis
957Embedding-Based Semantic Caching
958Prompt Prefix Caching
959Cache Invalidation Strategies
960Multi-Tier Caching Architecture
961Monitoring Cache Hit Rates
962Security and Privacy in Caching

921Understanding Stateless Architecture in LLM Applications
922Understanding Stateful Architecture in LLM Applications
923Trade-offs: Scalability and Simplicity
924Client-Side State Management
925Server-Side Session Storage
926Session Affinity and Load Balancing
927State Serialization and Token Limits
928Hybrid Architectures: Best of Both Worlds
929Session Expiration and Cleanup
930When to Choose Stateless vs Stateful
931Synchronous Request-Response Basics
932When to Use Synchronous Patterns
933Asynchronous Pattern Fundamentals
934Task Queues for LLM Workloads
935WebSockets for Real-Time Streaming
936Webhook-Based Completion Notifications
937Polling Patterns and Best Practices
938Background Processing with Workers
939Async/Await in Python for Concurrent Requests
940Timeout and Cancellation Handling
941User Experience Trade-offs
942Hybrid Patterns for Complex Workflows
943Choosing the Right Database for LLM Applications
944Session Storage for Conversational State
945Document Storage for User Data and Context
946Metadata and Application State Management
947Vector Database Integration Patterns
948Message Queues and Event Streaming
949Blob Storage for Large Context and Artifacts
950Database Sharding and Partitioning Strategies
951Transactional Consistency in AI Workflows
952Storage Cost Optimization and Data Lifecycle
953Why Caching Matters for LLM Applications
954Semantic vs Exact Caching
955Cache Key Design for Prompts
956In-Memory Caching with Redis
957Embedding-Based Semantic Caching
958Prompt Prefix Caching
959Cache Invalidation Strategies
960Multi-Tier Caching Architecture
961Monitoring Cache Hit Rates
962Security and Privacy in Caching

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Lesson 927 of 1,886·23. LLM Application Architecture PatternsPro lesson

State Serialization and Token Limits

Handle context window limits by serializing state, truncating history, or summarizing previous turns before sending to LLM.

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