Implementation of Retrieval-Augmented Generation (RAG) in Chatbot Systems for Enhanced Real-Time Customer Support in E-Commerce

This research study presents an advanced chatbot for e-commerce platforms using Retrieval-Augmented Generation (RAG), a technology that significantly enhances conversational AI by combining retrieval and generative techniques. E-commerce platforms handle diverse customer queries, including product inquiries, order tracking, and troubleshooting, where traditional chatbots often fail to provide accurate responses, leading to user dissatisfaction. The RAG-based chatbot addresses this by retrieving relevant information from sources like product catalogs, FAQs, and customer reviews and generating responses tailored to specific queries. This approach ensures accurate, contextually relevant answers that improve customer satisfaction, streamline service processes, and reduce errors. By leveraging the RAG framework, this solution provides robust, scalable customer support that enhances engagement and optimizes the e-commerce experience.