Advanced Leadership for Engineers and Professionals

Sagar Laishetti

Initially, I wasn’t sure what to expect from this course. As someone working across automotive and a bit of aerospace programs, most AI content I see is either hype or too academic. This one landed somewhere practical. The overview of how large language models work helped close a gap I had around why tools like ChatGPT behave inconsistently, which matters when you’re dealing with requirements flow-down or documentation tied to ISO 26262 or even DO‑178C style processes. One useful angle was seeing how generative AI can support early-phase tasks like requirements clarification, test case brainstorming, or summarizing CFD or simulation results for reviews. A real challenge during the course was separating realistic use cases from things that are still risky, especially around hallucinations when asking domain-specific questions about control systems or vehicle architecture. The most immediate takeaway was learning how to structure prompts with constraints and verification steps, instead of treating ChatGPT like a search engine. That alone made the outputs more usable on an active project. It’s not a replacement for engineering judgment, but it does save time in the margins. I can see this being useful in long-term project work.

edward pappoe Deputy Director engineering

Initially, I wasn’t sure what to expect from this course, especially given it was only an hour and I already use ChatGPT casually at work. Coming from an automotive background with some exposure to aerospace systems, I was curious whether it would actually add value beyond basic prompts. What worked well was the explanation of how large language models reason and where they break down. That helped me rethink how to use ChatGPT for tasks like drafting requirements for an automotive ECU update and summarizing aerospace-style verification documents. One challenge was translating the generic examples into engineering-specific workflows; the course doesn’t fully walk you through domain-heavy use cases like thermal analysis notes or failure mode discussions. Still, it highlighted the importance of structured prompts and iteration, which was a gap in my understanding. A practical takeaway was learning how to constrain outputs so they’re more usable for real projects—like asking for assumptions, limitations, or step-by-step logic instead of a polished answer. I’ve already applied this while reviewing design trade-offs and preparing internal technical summaries. The course isn’t deep, but it’s grounded enough to be useful for working engineers. It definitely strengthened my technical clarity.

Harit Naik Director - Global Innovation & Knowledge Management

Initially, I wasn’t sure what to expect from this course. Coming from a production engineering background, AI always felt a bit abstract, but the way it was tied to real industrial problems made it click. Topics like predictive maintenance using machine learning and computer vision for defect detection were especially relevant, since similar issues show up on our shop floor. The section on data preprocessing and feature selection was something I didn’t realize I was missing, and it filled a clear knowledge gap from my earlier, more theory-heavy exposure to AI. One challenge was wrapping my head around model selection trade-offs, especially when comparing neural networks versus simpler models for limited datasets. The course didn’t hide those limitations, which I appreciated. A practical takeaway was learning how to structure an end-to-end AI workflow, from collecting sensor data to validating model outputs before deployment. That directly helped on a small pilot we’re running for anomaly detection on rotating equipment. The content felt grounded in real constraints like data quality and compute limits, not ideal scenarios. It definitely strengthened my technical clarity.

Prerana Renavikar

Coming into this course, I had some prior exposure to the subject, mostly from dabbling with Python scripts and a few proof‑of‑concept models at work. What helped here was the way core topics like supervised learning (especially regression and classification) were tied directly to engineering use cases. The sections on time‑series forecasting for predictive maintenance and basic computer vision for inspection systems were particularly relevant to a manufacturing project I’m on. One challenge was getting through the model validation and hyperparameter tuning parts. Concepts like cross‑validation and overfitting weren’t new, but applying them correctly with noisy, real sensor data took a few attempts and some backtracking. That struggle actually mirrored what happens on the job, which made it useful rather than frustrating. A practical takeaway was a clear workflow for taking raw operational data, doing feature engineering, and deciding whether a simple model or a neural network is justified. That filled a knowledge gap between theory and what’s realistic under time and compute constraints. Parts of the course were uneven in difficulty, but the examples felt honest. Overall, it felt grounded in real engineering practice.