@inproceedings{murtaza-etal-2025-implementing,
    title = "Implementing Retrieval Augmented Generation Technique on Unstructured and Structured Data Sources in a Call Center of a Large Financial Institution",
    author = "Murtaza, Syed Shariyar  and
      Nie, Yifan  and
      Avan, Elias  and
      Soni, Utkarsh  and
      Liao, Wanyu  and
      Carnegie, Adam  and
      Mathias, Cyril John  and
      Jiang, Junlin  and
      Wen, Eugene",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-industry.48/",
    pages = "598--606",
    ISBN = "979-8-89176-194-0",
    abstract = "The retrieval-augmented generation (RAG) technique enables generative AI models to extract accurate facts from external unstructureddata sources. For structured data, RAG is further augmented by function calls to query databases. This paper presents an industrialcase study that implements RAG in a large financial institution`s call center. The study showcases experiences and architecture for ascalable RAG deployment. It also introduces enhancements to RAG for retrieving facts from structured data sources using data embeddings, achieving low latency and high reliability. Our optimized production application demonstratesan average response time of only 7.33 seconds. Additionally, the paper compares various open-source and closed-source models for answer generation in an industrial context."
}