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

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.