Automated GMP Compliance Training Content Validation and Generation via HyperScore-Driven Knowledge Graph Integration

**Abstract:** This research proposes a novel system for automating the validation and generation of GMP (Good Manufacturing Practice) compliance training content. Addressing the critical need for up-to-date and easily accessible training materials, our approach leverages a Multi-modal Data Ingestion & Normalization Layer coupled with a Semantic & Structural Decomposition Module to process diverse regulatory documents and training materials. A Multi-layered Evaluation Pipeline, utilizing Logical Consistency Engines, Execution Verification Sandboxes, and Novelty/Impact Assessment models, assigns a HyperScore to all content, reflecting its accuracy, relevance, and potential impact on operational compliance. Finally, a Human-AI Hybrid Feedback Loop integrates expert reviews with AI-driven debate to continuously refine content and optimize training effectiveness. This system presents a commercially viable solution to significantly reduce compliance risks, improve training efficiency, and ensure adherence to evolving GMP regulations within the pharmaceutical, biotechnology, and related industries.