Program Overview:

This intensive professional program bridges the gap between foundational generative modeling and high-level industrial AI implementation. The curriculum began with the mechanics of advanced prompt engineering, moving from basic conversational instructions to complex system-level architectures for Gemini and ChatGPT. Students mastered the implementation of Retrieval-Augmented Generation (RAG), focusing on document chunking strategies, vector retrieval precision, and context window management to eliminate hallucinations when working with private or updated datasets. A core highlight was the development of autonomous AI Agents, where students built systems capable of multi-step reasoning, planning, and tool-use through platforms like n8n and specialized APIs. The course culminated in multimodal engineering and model evaluation, featuring the "LLM as a judge" methodology to verify factual correctness and relevance across text, image, and data-driven outputs.