How IT professionals can build real AI-powered products — beyond chatbots and prompting.
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You already use ChatGPT, Claude and Copilot. But there is a large gap between using an AI tool and engineering an AI-powered product. Most developers can call an LLM API. Far fewer can say where AI belongs in a product, when RAG beats a database query, when an agent is the wrong answer, or what it costs to run any of it reliably.
This is a 90-minute practical session on how modern AI applications are actually built, with a live engineering demonstration rather than slides.
What to learn, in what order, and what to skip
How the pieces of a real AI product fit together
Know which one a given problem actually calls for
Applying it to the systems you already maintain
The expensive detours most developers take first
Lead Mentor · Machine Learning, Generative AI & Agentic AI
Designs the curriculum and leads the hands-on labs, helping participants think through problems the way an AI engineer does instead of memorising tools.
Real recordings from our labs: running AI apps, code and measured results.
A lab project: FastAPI backend, Chroma vector store and Llama 3 via Groq, answering questions about LangChain documentation.
A lab notebook: a local FAISS knowledge base, a RAG pipeline on Groq LLMs, and RAGAS metrics to evaluate the answers. Shown at 2.5× speed.
Yes. Registration is free and there is no obligation to buy anything.
No. If you work in IT you will get the most out of it, but no AI or ML background is needed.
Yes. The architecture and concepts apply across technology stacks.
Yes, a practical engineering demonstration — not a slide walkthrough.
Yes, there is a live Q&A section at the end.