session management

**Session management** in AI applications is the practice of **tracking and maintaining conversation state** across multiple interactions between a user and an AI system. It enables multi-turn conversations, personalization, and context continuity. **What Session State Includes** - **Conversation History**: All previous messages in the current conversation (user inputs and model responses). - **System Context**: The system prompt, user preferences, and any injected context. - **Metadata**: Session ID, user ID, timestamps, model version, token usage. - **Application State**: Shopping cart contents, form progress, selected options, or any task-specific state. - **Memory Summaries**: Compressed representations of earlier conversation turns for long sessions. **Session Management Challenges for LLMs** - **Context Window Limits**: LLMs have fixed context windows. As conversations grow long, older messages must be **truncated, summarized, or stored externally**. - **Stateless Models**: LLMs are inherently stateless — they don't remember previous requests. Session state must be **explicitly managed** by the application. - **Multi-Device**: Users may switch between devices and expect conversation continuity. - **Concurrency**: A user may have multiple simultaneous conversations with the same AI system. **Implementation Approaches** - **Server-Side Storage**: Store session data in a database (Redis, PostgreSQL, DynamoDB). Most common for production systems. - **Token-Based Sessions**: Encode minimal session state in a JWT or similar token passed with each request. - **Window Management**: Keep only the last N turns in context; summarize earlier turns into a running summary. - **Vector Store Memory**: Store conversation turns in a vector database and retrieve relevant past interactions via semantic search. **Best Practices** - **Session Timeouts**: Expire inactive sessions to free resources and protect privacy. - **Session Isolation**: Ensure one user cannot access another user's session data. - **Context Compression**: Use summarization to keep session context within token limits without losing important information. - **Graceful Recovery**: Handle the case where session state is lost — don't crash, ask the user to re-establish context. Session management is the **invisible infrastructure** that makes AI applications feel conversational rather than transactional.