%20have%20shown%20versatility%20in%20various%20Natural%20Language%20Processing%20(NLP)%20tasks%2C%20including%20their%20potential%20as%20effective%20question-answering%20systems.%20However%2C%20to%20provide%20precise%20and%20relevant%20information%20in%20response%20to%20specific%20customer%20queries%20in%20industry%20settings%2C%20LLMs%20require%20access%20to%20a%20comprehensive%20knowledge%20base%20to%20avoid%20hallucinations.%20Retrieval%20Augmented%20Generation%20(RAG)%20emerges%20as%20a%20promising%20technique%20to%20address%20this%20challenge.%20Yet%2C%20developing%20an%20accurate%20question-answering%20framework%20for%20real-world%20applications%20using%20RAG%20entails%20several%20challenges:%201)%20data%20availability%20issues%2C%202)%20evaluating%20the%20quality%20of%20generated%20content%2C%20and%203)%20the%20costly%20nature%20of%20human%20evaluation.%20In%20this%20paper%2C%20we%20introduce%20an%20end-to-end%20framework%20that%20employs%20LLMs%20with%20RAG%20capabilities%20for%20industry%20use%20cases.%20Given%20a%20customer%20query%2C%20the%20proposed%20system%20retrieves%20relevant%20knowledge%20documents%20and%20leverages%20them%2C%20along%20with%20previous%20chat%20history%2C%20to%20generate%20response%20suggestions%20for%20customer%20service%20agents%20in%20the%20contact%20centers%20of%20a%20major%20retail%20company.%20Through%20comprehensive%20automated%20and%20human%20evaluations%2C%20we%20show%20that%20this%20solution%20outperforms%20the%20current%20BERT-based%20algorithms%20in%20accuracy%20and%20relevance.%20Our%20findings%20suggest%20that%20RAG-based%20LLMs%20can%20be%20an%20excellent%20support%20to%20human%20customer%20service%20representatives%20by%20lightening%20their%20workload.)
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