Development of a hybrid method to enhance context memory for a chatbot application based on large language models - Bohdan, N.; Tvoroshenko, I.; Gorokhovatskyi, V.; Kobylin, O. (My University, 2025)
This paper addresses the critical challenge of maintaining long-term contextual coherence in LLM-based chatbots, particularly in scenarios demanding multi-stage reasoning and complex information recall. The core scientific novelty of this research lies in the development and justification of a Hybrid Methodology for Contextual Memory Enhancement. This methodology synergistically integrates the temporal continuity of recent messages’ context with the precision recall capabilities of RAG and AI analysis agent, effectively mitigating the common issue of contextual dilution over extended dialogues. The research involved the development of chatbot application implementing this method and subsequent rigorous scenario-based testing, which validated the superior performance of the proposed hybrid approach. The results provide definitive recommendations for optimizing LLM memory management, paving the way for more robust and reliable conversational AI systems capable of advanced, multi-turn reasoning and complex task resolution.
Keywords: artificial intelligence; chatbot; embeddings; generation; large language models; retrieval-augmented memory