AI-Powered Document Generation: Using NLP for Intelligent Data-To-Template Mapping

Abstract: Augmenting Automated Document Generation This paper introduces the Sandbox: Document Generating Engine, a novel, secure, and modular web application built with Python and Streamli (Achachlouei, A., Patil, M. A., Joshi, Q., Vair, T. & N. 2021). The primary research objective is to validate the feasibility and efficacy of augmenting Intelligent Document Processing (IDP) workflows by integrating Contemporary Large Language Models (LLMs) for semantic data-to-template mapping. Addressing the challenges of manual, time-consuming, and error-prone document creation, the system leverages Natural Language Processing (NLP) capabilities to analyze data uploaded in diverse formats (e.g., .csv, .xlsx, .txt) and automatically populate predefined document templates (Adhikari, P. R. 2018). The system features a robust secure authentication module utilizing bcrypt for password hashing and PostgreSQL for credential management. Our initial technical findings demonstrate high reliability, with Extraction Accuracy consistently over 95% across test documents. Furthermore, the system drastically reduced the time required for complex document creation, validating the capacity of LLM-enhanced IDP to yield substantial improvements in efficiency and productivity over simple rule-based methods. (Bitzenbauer, P. 2023).

Generative AI, Large Language Models (LLMs), Intelligent Document Processing (IDP), Automation, Template Mapping, Data Extraction, Python/Streamlit, Secure Authentication

1. Achachlouei, A., Patil, M. A., Joshi, Q., Vair, T. & N. (2021). Document Automation Architectures and Technologies: A Survey. arXiv. https://arxiv.org/abs/2109.02605 [Google Scholar] [Crossref]

2. Adhikari, P. R. (2018). Understanding of Plagiarism through Information Literacy: A Study among the Students of Higher Education of Nepal. Journal of Business and Social Sciences Research, 3(2), 165–181. https://doi.org/10.3126/jbssr.v3i2.28132 [Google Scholar] [Crossref]

3. AlAli, R., & Wardat, Y. (2024). Opportunities and Challenges of Integrating Generative Artificial Intelligence in Education. International Journal of Religion, 5(7), 784–793. https://doi.org/10.61707/8y29gv34 [Google Scholar] [Crossref]

4. Aldosari, S. A. M. (2020). The Future of Higher Education in the Light of Artificial Intelligence Transformations. International Journal of Higher Education, 9(3), 145. https://doi.org/10.5430/ijhe.v9n3p145 [Google Scholar] [Crossref]

5. Almahasees, Z., Khalil, M., & Am inzadeh, S. (2024). Students’ Perceptions of the Benefits and Challenges of Integrating ChatGPT in Higher Education. Pakistan Journal of Life and Social Sciences (PJLSS), 22(2), 3479–3494. https://doi.org/10.57239/PJLSS-2024-22.2.00256 [Google Scholar] [Crossref]

6. Archila, P. A., Ortiz, B. T., Truscott de Mejía, A.-M., & Molina, J. (2024). Thinking critically about scientific information generated by ChatGPT. Information and Learning Science. https://doi.org/10.1108/ILS-04-2024-0040 [Google Scholar] [Crossref]

7. Arora, S., Yang, S., Eyuboglu, B., Narayan, S., Hojel, A., Trummer, A., & E., I. R. (2023). Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes. Proc. VLDB Endow., 17(2), 92–104. https://doi.org/10.14778/3620359.3620366 [Google Scholar] [Crossref]

8. Athaluri, A. S., Manthena, S. V., K., M. V. S. R., Kesapragada, V., Yarlagadda, T., Dave, & Dudumpudi, R. T. S. (2023). Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References. Cureus, 15(12). https://doi.org/10.7759/cureus.49964 [Google Scholar] [Crossref]

9. Bakiri, H., Mbembati, H., & Tinabo, R. (2023). Artificial Intelligence Services at Academic Libraries in Tanzania: Awareness, Adoption and Prospects. University of Dar Es Salaam Library Journal, 18(2).https://doi.org/10.4314/udslj.v18i2.3 [Google Scholar] [Crossref]

10. Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 49(6), 893–905. https://doi.org/10.1080/02602938.2024.2335321 [Google Scholar] [Crossref]

11. Biswas, S., Jain, S., Morariu, R., Gu, V. L., Mathur, J., Wigington, P., Sun, C., & Uehida, T. (2024). DocSynthV2: A Practical Autoregressive Modelling for Document Generation. arXiv. https://arxiv.org/abs/2406.02492. [Google Scholar] [Crossref]

12. Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), ep430. https://doi.org/10.30935/cedtech/13176. [Google Scholar] [Crossref]

13. Borkovska, I., Kolosova, H., Kozubska, I., & Antonenko, I. (2024). Integration of AI into the Distance Learning Environment: Enhancing Soft Skills. Arab World English Journal, 1(1), 56–72. https://doi.org/10.24093/awej/ChatGPT.3 [Google Scholar] [Crossref]

14. Bozkurt, A. (2024). Tell Me Your Prompts and I Will Make Them True: The Alchemy of Prompt Engineering and Generative AI. Open Praxis, 16(2), 111–118. https://doi.org/10.55982/openpraxis.16.2.661 [Google Scholar] [Crossref]

15. Bradley, C. (2013). Information Literacy Articles in Science Pedagogy Journals. Evidence Based Library and Information Practice, 8(4), 78–92. https://doi.org/10.18438/B8JG76 [Google Scholar] [Crossref]

16. Cain, W. (2024). Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education. TechTrends, 68(1), 47–57. https://doi.org/10.1007/s11528-023-00896-0 [Google Scholar] [Crossref]

17. Carroll, A. J., & Borycz, J. (2024). Integrating large language models and generative artificial intelligence tools into information literacy instruction. The Journal of Academic Librarianship, 50(4), 102899.https://doi.org/10.1016/j.acalib.2024.102899 [Google Scholar] [Crossref]

18. ÇAYIR, A. (2023). A Literature Review on the Effect of Artificial Intelligence on Education. İnsan ve Sosyal Bilimler Dergisi, 6(2), 276–288. https://doi.org/10.53048/johass.1375684 [Google Scholar] [Crossref]

19. Lin, C.-H., & Cheng, C. P. (2024). Legal Documents Drafting with Fine-Tuned Pre-trained Large Language Model. arXiv. https://arxiv.org/abs/2406.08860 [Google Scholar] [Crossref]

20. Mohammadi, B., et al. (2024). Creativity Has Left the Chat: The Price of Debiasing Language Models. arXiv. https://arxiv.org/abs/2403.04595 [Google Scholar] [Crossref]

21. Mridul, M. A., Sloyan, I., Gupta, A., & Seneviratne, O. (2025). AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts. arXiv. https://arxiv.org/abs/2506.09633 [Google Scholar] [Crossref]

22. Nigam, S. K., Patnaik, B. D., Thomas, A. V., Shallum, N., Ghosh, K., & Bhattacharya, A. (2025). Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhiDastavej. International Journal of Law, Technology, and Management. https://doi.org/10.48550/arXiv.2506.09540 [Google Scholar] [Crossref]

23. Zhao, H., & Li, D. (2024). A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval–Augmented Generation. arXiv. https://arxiv.org/abs/2403.18560 [Google Scholar] [Crossref]

24. Zhang, Q., Huang, B., Jiang, V., Wang, J., Jiang, Z., He, L., & Zhang, C. (2024). Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction. ResearchGate. https://arxiv.org/abs/2403.11186 [Google Scholar] [Crossref]

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