Generative AI in Manufacturing
About This Course
This course is designed for technical and domain professionals in manufacturing who want to develop practical capabilities in applying generative Artificial Intelligence (AI) and agentic AI to real-world operational scenarios. It provides a structured and hands-on pathway from foundational concepts to the design, development and evaluation of working AI-driven solutions, and does not require prior attendance in related masterclasses.
Participants will acquire core competencies in generative AI and agentic AI fundamentals, including project scoping, risk considerations, and application design using large language models (LLMs). The course introduces key concepts such as prompt engineering, context grounding, and workflow orchestration, enabling learners to develop reliable and context-aware AI applications without the need for model fine-tuning.
Learners will explore how to incorporate enterprise and operational data into AI workflows to support knowledge-driven tasks such as information retrieval, decision support, and troubleshooting. Participants will be guided through structured use case discovery, where they identify and frame high-value opportunities within their own operational contexts, translating real-world challenges into implementable AI solution concepts.
The course further introduces agentic AI concepts, where learners extend their solutions into multi-step workflows that coordinate reasoning and tool usage to support structured tasks such as diagnostics, root cause analysis, and operational decision-making. Participants also gain exposure to low-code approaches for configuring agent capabilities, integrating tools, and orchestrating workflows to accelerate solution development.
The course culminates in a guided mini-project with assessment, where participants design, prototype, and present a manufacturing-focused agentic AI solution that demonstrates end-to-end integration of prompt design, knowledge grounding, and workflow orchestration.
What You'll Learn
- Analyse manufacturing scenarios using generative AI and agentic AI concepts to define problem statements, solution objectives, and expected value or impact for applications.
- Apply prompt engineering techniques and structured prompting approaches to design effective prompts, response formats, and basic guardrails for reliable AI outputs.
- Design AI application workflows by configuring key components such as model behaviour, context integration, and interaction logic for manufacturing use cases.
- Incorporate enterprise and operational data into AI systems to enable grounded, context-aware responses for tasks such as information retrieval and decision support.
- Evaluate AI system outputs and improve performance by identifying failure modes and refining prompts, workflows, or data inputs.
- Identify and prioritise high-value use cases within operational workflows, and translate them into structured AI solution proposals with clear scope, requirements, and expected outcomes.
- Develop agentic AI workflows that produce structured outputs (e.g. symptom → likely cause → recommended actions) and coordinate multiple steps or tools to support manufacturing decision-making.
- Prototype and configure AI-driven workflows using low-code approaches, including defining agent capabilities, tool usage, and interaction logic.
- Present the developed solution, including its design approach, results, limitations, and proposed improvements for future implementation or deployment.
Entry Requirements
Min. Diploma