Generative AI for Automotive Engineers

This course, Generative AI for Automotive Engineers, is designed for professionals seeking to embed AI-powered prompt engineering into core automotive workflows—from concept to production. The course begins by exploring how Generative AI integrates with mechanical and automotive contexts, followed by identifying decision points that can be converted into effective prompt sequences. It delves into the role of language models in CAD, simulation, and diagnostics, and addresses the inherent limitations of LLMs in precision tasks—providing methods to mitigate those gaps through structured prompt design.The second part focuses on crafting high-impact engineering prompts. Learners will practice writing prompts for technical, tabular, and dimensional outputs, embedding parameters and constraints effectively, and using system prompts to simulate roles like AI co-designer or virtual inspector. Techniques such as clarification loops, verification prompts, and expansion prompts are introduced to enhance reliability and control. Moving into CAD applications, the course teaches how to generate concept sketches and constraint-based designs, automate Bill of Material creation, iterate lightweight EV components, and produce CAD-ready outputs with dimensions and tolerances.In the simulation section, students write prompts for boundary condition setups, crash scenario generation, and thermal or structural model interpretation. For diagnostics, the course trains learners to interpret OBD-II and CAN codes, build NLP-based fault trees, compose intelligent maintenance prompts, and design end-to-end diagnostic chains. Manufacturing support is covered through process sheet generation, torque and sequence prompts, tooling instructions, and preventive maintenance flows. Learners also tackle visual defect classification in painted or welded surfaces, prompt-based checklist creation, NCR and CAPA automation, and quality log generation. The course concludes with reporting prompts that convert logs and designs