The comprehensive course is designed to take you from zero knowledge to becoming a proficient Applied AI Engineer to facilitate Retrieval-Augment Generation (RAG) techniques to enable domain adaptation for various LLMs. Whether you're a complete beginner or have some prior experience, this course will equip you with the essential skills and knowledge to thrive in this rapidly evolving field to help you build your generative AI solutions.
- Learn the fundamental concepts of generative AI and its key terms to keep to date with the latest trends and technologies
- Learn various RAG techniques from basic to advanced using LangChain and the latest platforms
- Acquire system-level thinking to architect and develop a complete RAG solution
- Understand how to evaluate and improve the overall RAG system from end to end
Hao-Yuan (Mark) is a research engineer based in Taiwan. He is a quantum AI and AI researcher working with the National Taiwan University and other research institutions in Taiwan to engineer novel quantum deep learning methods to accomplish machine learning utility. He is also an experienced full-stack engineer with a front-end in Next.js and Python backend for generative AI workloads.