ЗМІНА МОДЕЛІ РОБОТИ ЧАТ-БОТІВ У СФЕРІ ОБСЛУГОВУВАННЯ КЛІЄНТІВ

Abstract

In modern business, customer support stands as a pivotal element for successful client servicing. The operation of customer support involves providing assistance and resolving client issues regarding company products or services. In the current simplified model, the efficacy of technical support agent searches generally surpasses that of chatbots or human assistance. Filtering internal staff-only articles from those intended for clients poses a challenge, necessitating complex implementations that not all companies may afford. Previous-generation chatbots have shown limited effectiveness in replacing humans due to varying client articulations, necessitating human intervention. Conventional chatbots offer limited options: information retrieval based on client queries, problem narrowing through additional inquiries, and sending tailored instructions. However, maintaining and updating knowledge for both internal staff and clients poses resource-intensive challenges for large companies. An alternative emerges with LLM-based chatbots, automating communication with users via chat interfaces. LLM chatbots enhance speed and efficiency, reduce personnel costs, and ensure 24/7 client service. Yet, they require con- stant knowledge updating and careful handling to address a spectrum of issues effectively. Notably, LLM chatbots can handle complex articles, gathering step-by-step instructions from various sources. Still, undocumented issues or incorrect solutions may necessitate human assistance. The primary functional difference between LLM chatbots and conventional ones lies in their contextual and linguistic processing capabilities, making communication with LLM chatbots akin to human interaction. Despite advancements, the nuances of context and language remain pivotal for effective client servicing, particularly in technical support scenarios. Future exploration in this direction should focus on optimizing knowledge management and enhancing LLM chatbot capabilities for improved client satisfac- tion and operational efficiency."