Generative AI Fundamentals: Building Intelligent Applications

Generative artificial intelligence is reshaping how modern software is built and how knowledge work is automated. This text-based course equips you with the foundational concepts, terminology, and practical design patterns required to understand and implement generative models effectively. You will begin by establishing a solid grasp of core mechanics—including tokenization, embeddings, and transformer architectures—before moving on to structured workflows for building reliable AI-driven systems. What you'll learn: - Understand the foundational mechanics of large language models and generative neural networks - Apply advanced prompt engineering techniques to produce reliable, structured outputs - Integrate retrieval-augmented generation (RAG) concepts with vector databases for grounded context - Explore fine-tuning principles and evaluation methods to tailor models to specific tasks - Design secure, scalable workflows while addressing model hallucination and bias risks The course begins with foundational vocabulary and conceptual walkthroughs before guiding you through step-by-step code snippets, system architectures, and written implementation exercises. Designed specifically for beginners and developers exploring AI for the first time, no prior machine learning background is required. Start reading today to build your expertise in modern generative AI systems.