"AI context as a reflection of human memory To make a large language model (LLM) truly useful, everything begins with context management. Context is more than a single prompt, it is the entire accumulation of conversation series. Every user input, combined with every AI response, forms the evolving environment in which meaning is created. In practice, context behaves a lot like memory. Just as human memory is organized, the context of an AI system can also be understood as a layered structure."
"Just as human memory is organized, the context of an AI system can also be understood as a layered structure. Human memory is often described in three parts: Semantic memory: long-term knowledge, concepts, and facts - what I know. Working memory: temporary information needed for the current task - what I am processing right now. Long-term memory: accumulated experiences, preferences, and patterns - what I tend to do."
Context management underpins effective LLM interactions by treating context as the accumulated sequence of user inputs and AI responses. Context functions like human memory, forming an evolving environment where meaning emerges. Context can be organized in layers analogous to human memory: semantic memory holds long-term facts and knowledge; working memory holds temporary information needed for current tasks; long-term memory stores accumulated experiences, preferences, and patterns. UX should expose and manage these layers to support retrieval, relevance, and continuity across conversations, enabling the system to maintain useful history, apply persistent knowledge, and focus on immediate task-relevant data.
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