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Sep 08, 2025•8 min read

How to create Longterm and Shorterm memory of chatbots.

Designing chatbot memory requires balancing short-term context and long-term knowledge. Short-term memory is managed through context engineering—sliding windows, rolling summaries, and structured state—ensuring coherent conversations. Long-term memory relies on Retrieval-Augmented Generation (RAG), retrieving grounded facts from vector databases, profiles, and past interactions to reduce hallucinations and provide continuity across sessions. A robust architecture combines input normalization, memory retrieval, context construction, and post-processing. Effective systems use hybrid retrieval, reranking, and compression to fit token budgets, while enforcing privacy and preventing stale or redundant facts. Together, RAG and context engineering enable attentive, reliable, and trustworthy conversational AI at scale.

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