"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."
"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 is the foundation of useful large language model interactions. Context encompasses the full accumulation of conversational turns rather than a single prompt; each user input together with each AI response creates an evolving environment where meaning emerges. Context functions analogously to human memory and can be modeled as layered systems. The layers map to semantic memory (long-term knowledge, concepts, facts), working memory (temporary information for the current task), and long-term memory (accumulated experiences, preferences, and patterns). Designing LLM interfaces requires handling these layers to preserve relevant information, support ongoing tasks, and leverage user history.
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