Generative AI for Quality Control Analysts

About this course

This comprehensive course on Generative AI for Quality Control Analysts in Manufacturing and Production is designed to empower quality professionals with cutting-edge tools and methodologies to transform traditional quality systems into intelligent, predictive, and highly automated operations. Starting with a foundational understanding of what Generative AI is and how it intersects with industrial quality, the course contrasts traditional reactive quality control practices with AI-augmented approaches that enable real-time defect detection, analysis, and documentation.Learners will gain a practical overview of leading GenAI tools such as ChatGPT, Claude, and Gemini, and explore their relevance in automating key quality functions—from inspection reporting and SOP generation to CAPA documentation and audit readiness. Special attention is given to structuring prompts for manufacturing environments, differentiating between instructional and analytical prompts, and building reusable templates for inspections, NCRs (Non-Conformance Reports), and CAPAs. The course also addresses advanced capabilities like prompt chaining for generating full inspection reports and leveraging large language models (LLMs) for identifying defect patterns, suggesting 5 Whys analysis, and building risk matrices.Through a practical lens, the course covers integration of GenAI with MES, QMS, and PLM systems, enabling real-time monitoring, traceability, and AI-based alert generation from machine logs. Visual inspection is enhanced through integration with vision systems, where GenAI aids in defect classification, annotation, and image-based reporting. The course also guides learners on creating AI-generated control charts, summarizing statistical quality metrics like Cp, Cpk, and SPC data, and auto-generating ISO 9001 and IATF 16949 compliance documents.Real-world case studies from food, steel, semiconductor, and textile industries illustrate how GenAI drives digital transformation in quality. A

Course objectives

• equip quality professionals with AI methodologies
• transform traditional quality processes into automated and predictive systems
• enhance defect detection and classification