IDEAS home Printed from https://ideas.repec.org/a/vrs/poicbe/v18y2024i1p209-222n1004.html
   My bibliography  Save this article

Artificial Intelligence Text Processing Using Retrieval-Augmented Generation: Applications in Business and Education Fields

Author

Listed:
  • Posedaru Bogdan-Stefan

    (Bucharest University of Economic Studies, Bucharest, Romania)

  • Pantelimon Florin-Valeriu

    (Bucharest University of Economic Studies, Bucharest, Romania)

  • Dulgheru Mihai-Nicolae

    (Bucharest University of Economic Studies, Bucharest, Romania)

  • Georgescu Tiberiu-Marian

    (Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

The article studies the current text processing tools based on Artificial Intelligence. A literature review is done emphasizing the dynamic evolution of AI-powered text analytics, having as its central tool ChatGPT and its capabilities. The focus is centered on the techniques and methods that are using embeddings in order to improve large language models (LLMs). In this paper is analyzed the current situation of the literature in terms of text processing using Retrieval-Augmented Generation and is highlighted the potential of this technology to enhance the interpretability and trust in applications critical, such as those related to education or business. AI has revolutionized natural language processing (NLP), which facilitated the machines to interpret and generate text efficiently and accurately. In addition, large language models with external knowledge bases have been developed. These are used to produce more accurate and contextually relevant text responses. This approach is called Retrieval-Augmented Generation (RAG is one of the most significant recent advancements in this field. Based on our study, two use cases are implemented to show the applicability of our study: one related to education and one related to business IT-related documents. The methodology describes the techniques used. This includes retrieval-augmented generation and embedding stored using vector databases. Our custom models are evaluated by comparing them to the general ones, without embeddings, showing superior performance. The article highlights remarkable progress in Retrieval-Augmented Generation (RAG), which is used for AI text processing with a focus on business and education fields. Further in this paper, many of the most significant highlights are presented, which include a scalable framework for AI applications, a new integration of Retrieval-Augmented Generation and embeddings, practical application demonstrations, bridging gaps in the analysis op AI text, significant development in AI performance and optimizing educational and business processes.

Suggested Citation

  • Posedaru Bogdan-Stefan & Pantelimon Florin-Valeriu & Dulgheru Mihai-Nicolae & Georgescu Tiberiu-Marian, 2024. "Artificial Intelligence Text Processing Using Retrieval-Augmented Generation: Applications in Business and Education Fields," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 209-222.
    • Handle: RePEc:vrs:poicbe:v:18:y:2024:i:1:p:209-222:n:1004
    DOI: 10.2478/picbe-2024-0018
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/picbe-2024-0018
    Download Restriction: no
    File URL: https://libkey.io/10.2478/picbe-2024-0018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Konstantinos I. Roumeliotis & Nikolaos D. Tselikas, 2023. "ChatGPT and Open-AI Models: A Preliminary Review," Future Internet, MDPI, vol. 15(6), pages 1-24, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ali, Omar & Murray, Peter A. & Momin, Mujtaba & Dwivedi, Yogesh K. & Malik, Tegwen, 2024. "The effects of artificial intelligence applications in educational settings: Challenges and strategies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    2. Konstantinos I. Roumeliotis & Nikolaos D. Tselikas & Dimitrios K. Nasiopoulos, 2023. "Unveiling Sustainability in Ecommerce: GPT-Powered Software for Identifying Sustainable Product Features," Sustainability, MDPI, vol. 15(15), pages 1-26, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:poicbe:v:18:y:2024:i:1:p:209-222:n:1004. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.