INTRODUCTION TO AI & QUALITY 4.0
- Overview of AI’s role in modern Quality Management and the shift to Quality 4.0.
- Key concepts of Generative AI, LLMs, and foundational terminologies.
- Challenges and opportunities of LLM adoption in quality assurance.
LLM PROMPT ENGINEERING FOR QUALITY EFFICIENCY
- Mastery of structured prompt design (Role, Task, Context) for reliable LLM output.
- The use of Gemini/ChatGPT to draft, review, and standardize QMS documentation (SOPs, work instructions).
- Leverage of LLMs for rapid data processing and summarization of text-based quality data (e.g., audit reports).
AI-ACCELERATED PROBLEM SOLVING
- Application of Claude/Gemini to structure and facilitate Root Cause Analysis (RCA) using methods like 5 Whys.
- Brainstorming and detailing Failure Modes and Effects (FMEA) for processes using AI assistance.
- Translation of AI analysis into clear, accountable Corrective and Preventive Actions (CAPA).
AI QUALITY GOVERNANCE & RISK MANAGEMENT
- Identification and mitigation of LLM-specific risks, including data leakage and hallucination.
- Establishment of governance frameworks for the ethical and responsible use of AI in quality decisions.
- The careful balancing of innovation (using AI for analysis) with compliance (verification and human review).
AI FOR CONTINUOUS IMPROVEMENT & COMMUNICATION
- The utilization of AI tools for basic process mapping, identifying non-value-added steps, and waste reduction (Lean).
- Tailoring of quality communication using LLMs to simplify technical audit findings for different stakeholder groups.
- Development of an AI Quality Strategy and roadmap for organizational quality transformation.
