Large Language Models (LLMs) like GPT-4o operate statelessly by default, meaning each API call is independent. This lack of long-term memory makes it necessary to explicitly pass previous messages to maintain continuity in conversations. If this historical context is not handled correctly, interactions can become disjointed, leading to responses that are either irrelevant or lack necessary details. As a result, implementing a managed memory system is crucial to ensuring meaningful interactions in applications that utilize LLMs.
In API-based LLM integrations, models don't retain any memory between requests. Each prompt is interpreted in isolation, emphasizing the need for continuous context sharing.
This is why memory in LLM applications often feels inconsistent. If past context isn't reconstructed properly, models may cling to irrelevant details or lose critical information.
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