Intelligent system for generating quality reports based on the synergy of machine learning and large language models: overlapping the gap between diagnostics and the quality management system

The article examines the problem of the gap between the detailed results of machine learning in the tasks of quality diagnostics in mechanical engineering and the needs of quality management systems in complex, interpretable and standardized reporting. An intelligent system based on synergistic integration of distributed machine learning modules for local diagnostics and large language models for automatic generation of context-dependent reports of the quality management system is proposed. The methodology, system architecture, algorithms of aggregation of diagnostic information, techniques of engineering of proxies for large language model and mechanisms of verification of generated reports are described, including factchecking for minimization of «hallucinations».The results of the pilot testing on typical quality control tasks confirm system performance, high accuracy of machine learning modules components and ability to generate factually accurate, relevant and compliant quality management system reports. The proposed approach allows to effectively bridge the gap between technical diagnosis and management needs of quality management system, automating the process of forming intellectual reporting on quality.