Enhancing Technical Document Compliance Review through a Context-Aware Generative AI Framework

Authors

  • Prashant Gulave Department of Computer Science, JSPM’s Rajarshi Shahu College of Engineering, Pune, Maharashtra, India Author
  • Dr. Kavita Moholkar Department of Computer Science, JSPM’s Rajarshi Shahu College of Engineering, Pune, Maharashtra, India Author

DOI:

https://doi.org/10.32628/CSEIT25113329

Abstract

The rigorous review of technical documentation for compliance with linguistic, domain-specific, and document- type standards is a critical, yet often labor-intensive and error-prone process. This paper presents a novel Generative AI (GenAI) based system designed to automate and significantly enhance the accuracy of technical document compliance checking. Our framework leverages Transformer-based Generative AI models within a hybrid architecture that synergizes Retrieval-Augmented Generation (RAG), semantic rule interpretation, and deep contextual analysis derived from a document graph. The system integrates three distinct layers of rule enforcement: language grammar, domain/standards specific rules, and document type specific requirements. A continuous human-in-the-loop feedback mechanism, driven by Reinforcement Learning from Human Feedback (RLHF), ensures iterative model refinement and rule base adaptation. The system provides a comprehensive compliance score and actionable, granular, and context-aware review comments. This paper details the implemented methodologies and materials, focusing on the strategies employed for achieving high accuracy and reliability in automated technical document review.

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References

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Published

24-05-2025

Issue

Section

Research Articles

How to Cite

[1]
Prashant Gulave and Dr. Kavita Moholkar, “Enhancing Technical Document Compliance Review through a Context-Aware Generative AI Framework”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 11, no. 3, pp. 740–745, May 2025, doi: 10.32628/CSEIT25113329.