DOI - Mendel University Press

DOI identifiers

DOI: 10.11118/ecos2026_037

WEB APPLICATION FOR GENERATING TECHNICAL DOCUMENTS USING LARGE LANGUAGE MODELS

Martin Šulc1, Ivo Pisařovic1
1 Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic

This paper investigates the viability of leveraging large language models (LLMs) to generate formal corporate documents, moving beyond rigid, traditional template-based approaches. The study aims to verify whether combining reference samples, natural language instructions, and parallel multi-model processing can yield a practical enterprise tool. The proposed microservices architecture utilizes a Node.js/LangChain backend to abstract calls across four LLM providers (OpenAI, Anthropic, Gemini, and Ollama). This is supported by Python microservices for input parsing and multi-format document generation (Word, PDF, Excel), a Vue.js frontend wizard, and PostgreSQL/Redis persistence. System validation involved end-to-end scenarios across five document types, benchmarking seven models, and user testing with ten participants. Results demonstrate the solution’s efficacy. Furthermore, comparative analysis reveals that local open-source models achieve generation quality comparable to mid-range cloud services for Czech-language tasks, albeit with trade-offs in increased latency and initial hardware investments.

Keywords: large language models, document generation, web application, microservices

pages: 338-347, online: 2026



References

  1. AZURE AI. 2025. Azure AI Document Intelligence documentation. Microsoft. [Accessed: 2026-02-14] https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/
  2. BIELAK, D., BOREK, K., PLECHAWSKA-WÓJCIK, M. 2022. Web application performance analysis using Angular, React and Vue.js frameworks. Journal of the Computer Sciences Institute. 23, 100-106. https://doi.org/10.35784/jcsi.2827 Go to original source...
  3. BRYNJOLFSSON, E., LI, D., RAYMOND, L. R. 2025. Generative AI at Work. Quarterly Journal of Economics. 140(2), 889-942. https://doi.org/10.1093/qje/qjae044 Go to original source...
  4. CHANG, Y., WANG, X., WANG, J., WU, Y., ZHU, K., CHEN, H., YANG, L. et al. 2024. A Survey on Evaluation of Large Language Models. ACM Transactions on Intelligent Systems and Technology. 15(3), 1-45. https://doi.org/10.1145/3641289 Go to original source...
  5. DOCUPILOT. 2026. Docupilot. [Accessed: 2026-02-19]. https://www.docupilot.com
  6. DWIVEDI, Y. K., KSHETRI, N., HUGHES, L., SLADE, E. L., JEYARAJ, A., KAR, A. K. et al. 2023. "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management. 71, art.102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 Go to original source...
  7. HODA, R., SALLEH, N., GRUNDY, J., TEE, H. M. 2017. Systematic literature reviews in agile software development: A tertiary study. Information and Software Technology. 85, 60-70. https://doi.org/10.1016/j.infsof.2017.01.007 Go to original source...
  8. JAMSHIDI, P., PAHL, C., MENDONÇA, N. C., LEWIS, J. W., TILKOV, S. 2018. Microservices: The Journey So Far and Challenges Ahead. IEEE Software. 35(3), 24-35. https://doi.org/10.1109/MS.2018.2141039. Go to original source...
  9. KALUŽA, M., VUKELIĆ, B. 2018. Comparison of front-end frameworks for web applications development. Zbornik Veleučilišta u Rijeci. 6(1), 261-282. https://doi.org/10.31784/zvr.6.1.19 Go to original source...
  10. KE, W., ZHENG, Y., LI, Y., XU, H., NIE, D., WANG, P., HE, Y. 2025. Large Language Models in Document Intelligence: A Comprehensive Survey, Recent Advances, Challenges, and Future Trends. ACM Transactions on Information Systems. 44, 1-64. https://doi.org/10.1145/3768156 Go to original source...
  11. KOCOŃ, J., CICHECKI, I., KASZYCA, O., KOCHANEK, M., SZYDŁO, D., BARAN, J. et al. 2023. ChatGPT: Jack of all trades, master of none. Information Fusion. 99, art.101861. https://doi.org/10.1016/j.inffus.2023.101861 Go to original source...
  12. LIU, P., YUAN, W., FU, J., JIANG, Z., HAYASHI, H., NEUBIG, G. 2023. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys. 55(9), 1-35. https://doi.org/10.1145/3560815 Go to original source...
  13. MARKITDOWN. 2024. MarkItDown - Python tool for converting files and office documents to Markdown. Microsoft. [Accessed: 2026-03-14] https://github.com/microsoft/markitdown
  14. MERKEL, D. 2014. Docker: Lightweight Linux Containers for Consistent Development and Deployment. Linux Journal. 2014(239).
  15. MIN, B., ROSS, H., SULEM, E., VEYSEH, A. P. B., NGUYEN, T. H., SAINZ, O., AGIRRE, E., HEINTZ, I., ROTH, D. 2023. Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey. ACM Computing Surveys. 56(2), 1-40. https://doi.org/10.1145/3605943 Go to original source...
  16. NEWMAN, S. 2015. Building Microservices: Designing Fine-Grained Systems. Sebastopol: O'Reilly Media. ISBN 978-1-491-95035-7
  17. NIELSEN, J. 2024. Usability Testing 101. Nielsen Norman Group. [Accessed: 2026-04-15] https://www.nngroup.com/articles/usability-testing-101/
  18. NOY, S., ZHANG, W. 2023. Experimental evidence on the productivity effects of generative artificial intelligence. Science. 381(6654), 187-192. https://doi.org/10.1126/science.adh2586 Go to original source...
  19. OLLAMA. 2026. Ollama - Get up and running with large language models. Ollama. [Accessed: 2026-03-08]. https://ollama.com/
  20. PATTERSON, D., GONZALEZ, J., HÖLZLE, U., LE, Q., LIANG, C., MUNGUIA, L.-M., ROTHCHILD, D., SO, D. R., TEXIER, M., DEAN, J. 2022. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Computer. 55(7), 18-28. https://doi.org/10.1109/MC.2022.3148714 Go to original source...
  21. SCHWARTZ, R., DODGE, J., SMITH, N. A., ETZIONI, O. 2020. Green AI. Communications of the ACM. 63(12), 54-63. https://doi.org/10.1145/3381831 Go to original source...
  22. ŠULC, M. 2026. Webová aplikace pro generování formálních dokumentů za podpory AI. Diploma thesis. Brno: Mendel University in Brno.
  23. TOPSAKAL, O., AKINCI, T. C. 2023. Creating Large Language Model Applications Utilizing LangChain: A Primer on Developing LLM Apps Fast. In: Proceedings of the International Conference on Applied Engineering and Natural Sciences (ICAENS), pp. 1050-1056. Go to original source...
  24. WRITER. 2026. Writer - The enterprise AI platform. [Accessed: 2026-02-21]. https://writer.com
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