conversation-memory

To persist context across LLM conversations, use a tiered memory system that manages short-term, long-term, and entity memory. This approach stores and retrieves past interactions, allowing your assistant to recall details from prior sessions.

Entity memory tracks specific facts about users or objects within AI assistants. You need it when memory of past interactions influences current responses, such as in multi-session support bots or personal assistants requiring context across months.

Implement memory retrieval and consolidation by applying memory-aware prompting and tiered retrieval workflows. This consolidates short-term interactions into long-term memory, ensuring relevant historical context is surfaced for multi-turn tasks.

Yes, persistent memory systems support strict isolation to prevent context leakage between users. This ensures that memory retrieval and persistence workflows remain segregated, maintaining privacy across multi-session support and long-running chats.

The best way to structure memory-aware prompting for customer support bots is to integrate tiered memory types with retrieval workflows. This allows the assistant to dynamically pull relevant long-term or entity memory into the prompt context.

Your LLM forgets previous session details because it lacks memory persistence. Without a system to manage and consolidate short-term, long-term, and entity memory, the assistant cannot retrieve context from past interactions.