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 have shown versatility in various Natural Language Processing (NLP) tasks%2C including their potential as effective question-answering systems. However%2C to provide precise and relevant information in response to specific customer queries in industry settings%2C LLMs require access to a comprehensive knowledge base to avoid hallucinations. Retrieval Augmented Generation (RAG) emerges as a promising technique to address this challenge. Yet%2C developing an accurate question-answering framework for real-world applications using RAG entails several challenges: 1) data availability issues%2C 2) evaluating the quality of generated content%2C and 3) the costly nature of human evaluation. In this paper%2C we introduce an end-to-end framework that employs LLMs with RAG capabilities for industry use cases. Given a customer query%2C the proposed system retrieves relevant knowledge documents and leverages them%2C along with previous chat history%2C to generate response suggestions for customer service agents in the contact centers of a major retail company. Through comprehensive automated and human evaluations%2C we show that this solution outperforms the current BERT-based algorithms in accuracy and relevance. Our findings suggest that RAG-based LLMs can be an excellent support to human customer service representatives by lightening their workload.?quality=80&w=800)
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