Generative AI for Developers

ʼI‘m glad to share that I have successfully completed the Generative AI for Developers course

This journey has been an incredible learning experience, deepening my knowledge of Generative AI, LLM applications, and AI-powered development workflows. The course covered essential concepts and hands-on projects, allowing me to work with cutting-edge AI frameworks and best practices for real-world AI applications.

💡 Key Topics & Technologies Explored:

– Building Generative AI Applications (RAGs, Fine-tuning, APIs)
– AI Model Deployment & MLOps Best Practices
– Cloud Infrastructure for AI & Data Engineering
– Automated Testing for LLMOps
– Advanced Prompt Engineering & Model Evaluation
– AWS & Cloud-Based AI Services

Beyond just learning, I had the honor of contributing back as a guest lecturer in one of the course workshops, sharing insights and experiences with fellow learners! 🎤💡

A huge thank you to SET University, Artem Kobrin, Rostyslav Myronenko Heorhii Kalaichev and other instructors for designing such a fantastic program! Looking forward to applying this knowledge in future AI-driven projects.  

Tell us about your previous experience in Al/ML. Why SET?

I’m a Data Engineer with 5+ years of experience. When LLMs started gaining traction, our company decided to grow its Al expertise.

We tried hiring an Al engineer – but quickly realized it wasn’t clear what such a role should actually look like. That’s when it became obvious: there’s a big difference between a “classic” ML engineer and someone who works specifically with LLMs.

To dive deeper into this field and make sense of the landscape, I joined the Generative Al for Developers micromaster’s program at SET University.

Share your general impressions of the program

The course was incredibly valuable — and honestly, quite impressive. Of course, like in any intense learning experience, there were moments when things didn’t go perfectly – both for participants and instructors. But that only made it more real.

It showed how quickly teams can identify and solve challenges together. Coming from a non-Al background, I had my doubts. Would I understand anything at all?

But the workshops and materials, delivered by experts with hands-on project experience, helped me get a clear picture of how things actually work.

And the final projects? Some of them were genuinely inspiring.

Share your biggest takeaways from the course

Before the course, LLM-related projects felt overwhelmingly complex. I can’t say everything suddenly became easy – but it definitely became clear. Al projects stopped feeling like a ‘black box’.

The final presentations showcased a wide – and sometimes unexpected – range of use cases for LLMs

I’m satisfied with the course; it was quite informative. I had the opportunity to ask the instructors many questions on topics that interested me, which was probably the most important thing for me. The theoretical sessions were good, but the initial topics were very basic for beginners, in my opinion. Toward the end, though, there were truly challenging and interesting topics.

As for advice, I’d only say that before each lesson, it’s better to do some self-study—Google, read about the topic that will be covered. That way, the material will be much easier to remember, especially during the later lessons.