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The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges

Author

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  • Ajay Bandi

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA)

  • Pydi Venkata Satya Ramesh Adapa

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA)

  • Yudu Eswar Vinay Pratap Kumar Kuchi

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA)

Abstract

Generative artificial intelligence (AI) has emerged as a powerful technology with numerous applications in various domains. There is a need to identify the requirements and evaluation metrics for generative AI models designed for specific tasks. The purpose of the research aims to investigate the fundamental aspects of generative AI systems, including their requirements, models, input–output formats, and evaluation metrics. The study addresses key research questions and presents comprehensive insights to guide researchers, developers, and practitioners in the field. Firstly, the requirements necessary for implementing generative AI systems are examined and categorized into three distinct categories: hardware, software, and user experience. Furthermore, the study explores the different types of generative AI models described in the literature by presenting a taxonomy based on architectural characteristics, such as variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, transformers, language models, normalizing flow models, and hybrid models. A comprehensive classification of input and output formats used in generative AI systems is also provided. Moreover, the research proposes a classification system based on output types and discusses commonly used evaluation metrics in generative AI. The findings contribute to advancements in the field, enabling researchers, developers, and practitioners to effectively implement and evaluate generative AI models for various applications. The significance of the research lies in understanding that generative AI system requirements are crucial for effective planning, design, and optimal performance. A taxonomy of models aids in selecting suitable options and driving advancements. Classifying input–output formats enables leveraging diverse formats for customized systems, while evaluation metrics establish standardized methods to assess model quality and performance.

Suggested Citation

  • Ajay Bandi & Pydi Venkata Satya Ramesh Adapa & Yudu Eswar Vinay Pratap Kumar Kuchi, 2023. "The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges," Future Internet, MDPI, vol. 15(8), pages 1-60, July.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:260-:d:1207623
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    References listed on IDEAS

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    1. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.
    2. Xingyu Zhou & Zhisong Pan & Guyu Hu & Siqi Tang & Cheng Zhao, 2018. "Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, April.
    3. Pratyush Muthukumar & Jie Zhong, 2021. "A Stochastic Time Series Model for Predicting Financial Trends using NLP," Papers 2102.01290, arXiv.org.
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    2. Jiaqi Li & Qinchuan Liu, 2025. "Application of Generative Artificial Intelligence AIGC Technology Under Neural Network Algorithm in Game Character Art Design," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 4652-4683, March.
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    4. Rafner, Janet & Guloy, Ryan Q & Wen, Eden & Chiodo, Catherine & Sherson, Jacob Friis, 2025. "From Interaction to Collaboration: How Hybrid Intelligence Enhances Chatbot Feedback," OSF Preprints jhefx_v1, Center for Open Science.
    5. Hyun Yong Ahn, 2024. "AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    6. TANGI Luca & COMBETTO Marco & HUPONT TORRES Isabelle & FARRELL Eimear & SCHADE Sven, 2024. "The potential of generative AI for the public sector: current use, key questions and policy considerations," JRC Research Reports JRC139825, Joint Research Centre.
    7. Sarah V. Bentley, 2025. "Knowing you know nothing in the age of generative AI," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-7, December.
    8. Nikša Alfirević & Daniela Garbin Praničević & Mirela Mabić, 2024. "Custom-Trained Large Language Models as Open Educational Resources: An Exploratory Research of a Business Management Educational Chatbot in Croatia and Bosnia and Herzegovina," Sustainability, MDPI, vol. 16(12), pages 1-18, June.
    9. Shaotong Qi & Yubo Cheng & Zhiyuan Li & Jiaxin Wang & Huaiyi Li & Chunwei Zhang, 2024. "Advanced Deep Learning Techniques for Battery Thermal Management in New Energy Vehicles," Energies, MDPI, vol. 17(16), pages 1-38, August.
    10. Rezazadeh, Arash & Kohns, Marco & Bohnsack, René & António, Nuno & Rita, Paulo, 2025. "Generative AI for growth hacking: How startups use generative AI in their growth strategies," Journal of Business Research, Elsevier, vol. 192(C).
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