Generative AI-Based Intelligent Information Retrieval and Knowledge Management
Keywords:
Generative Artificial Intelligence, Information Retrieval, Knowledge Management, Large Language Models, Natural Language Processing, Semantic Search, Intelligent SystemsAbstract
The rapid growth of digital information has created significant challenges for organizations in retrieving, organizing, and managing knowledge effectively. Traditional information retrieval systems primarily depend on keyword matching and predefined indexing techniques, which may not adequately understand the context, intent, and relationships within large collections of unstructured information. Generative Artificial Intelligence (GenAI), particularly large language models, provides new opportunities for improving information retrieval and knowledge management by enabling semantic understanding, natural-language interaction, contextual response generation, and automated knowledge organization. This paper proposes a Generative AI-Based Intelligent Information Retrieval and Knowledge Management framework that integrates document processing, semantic representation, intelligent retrieval, generative response generation, and knowledge management. The proposed approach allows users to submit natural-language queries and obtain contextually relevant responses from organizational knowledge repositories. The framework also supports automatic summarization, knowledge classification, question answering, and continuous knowledge updating. The paper discusses the architecture, major components, operational workflow, benefits, challenges, and potential applications of the proposed framework. The study demonstrates that the integration of Generative AI with information retrieval can improve accessibility, reduce information-search effort, and support more effective organizational knowledge utilization. However, issues related to hallucination, data privacy, security, knowledge freshness, and response reliability must be addressed for dependable real-world deployment.
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1. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N. S., Chen, A., Creel, K., Davis, J. Q., Demszky, D., … Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://arxiv.org/abs/2108.07258
2. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, H., & Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv. https://arxiv.org/abs/2312.10997
3. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
5. Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., … Wen, J.-R. (2023). A survey of large language models. arXiv. https://arxiv.org/abs/2303.18223
6. Bhagwat, V. B. (2024). A simplified transition from EBS Payroll to Cloud Payroll: Benefits and Drawbacks. Journal of Computational Analysis and Applications, 33(6), 463-474.
7. Bhagwat, V. B. (2025). Simplifying Payroll Balance Conversions in Payroll Systems Implementation through the Use of Generative AI.
8. Prodduturi, S. M. (2025). Cryptography in iOS: A study of secure data storage and communication techniques. International Journal on Science and Technology, 16(1).
9. Prodduturi, S. M. K. (2025). Opportunities and Challenges for iOS Developers in Exploring the Integration of Augmented Reality Technologies. International Journal of Engineering Science and Advanced Technology (IJESAT), 25(4), 200-207.
10. Narapareddy, V. S. R., & Yerramilli, S. K. (2023). ARTIFICIALINTELLIGENCE INCIDENTFORECASTING. International Journal of Engineering TechnologyResearch & Management (IJETRM), 7(12), 551-559.
11. Narapareddy, V. S. R. (2022). Strategies for Integrating Services with External Systems Via Rest & Soap. Universal Library of Engineering Technology,(Issue).
12. Todupunuri, A. (2025). The role of human-centric AI in building trust in digital banking ecosystems. International Journal of Innovative Science and Research Technology, 10(1), 1281-1286.
13. Todupunuri, A. (2025). Improving Customer Experience With Modern Banking Solutions. SSRN Electronic Journal. https://doi. org/10.2139/ssrn, 5120615.
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Copyright (c) 2026 D. Nirosha (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
