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Lewis, P., et al. (2020) Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

has been cited by the following article:

• TITLE: Efficient Large Language Model Application Development: A Case Study of Knowledge Base, API, and Deep Web Search Integration

  AUTHORS: Xiangyu Wang, Yan Tan, Tao Yang, Meng Yuan, Shaohan Wang, Min Chen, Feiyang Ren, Zijian Zhang, Yuqi Shao

  KEYWORDS: Large Language Model, Knowledge Base, API Integration, Web Retrieval, Application Development

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.12, December 30, 2024

  ABSTRACT: This paper presents a reference methodology for process orchestration that accelerates the development of Large Language Model (LLM) applications by integrating knowledge bases, API access, and deep web retrieval. By incorporating structured knowledge, the methodology enhances LLMs’ reasoning abilities, enabling more accurate and efficient handling of complex tasks. Integration with open APIs allows LLMs to access external services and real-time data, expanding their functionality and application range. Through real-world case studies, we demonstrate that this approach significantly improves the efficiency and adaptability of LLM-based applications, especially for time-sensitive tasks. Our methodology provides practical guidelines for developers to rapidly create robust and adaptable LLM applications capable of navigating dynamic information environments and performing effectively across diverse tasks.