Overwhelmed by the explosion of generative AI tools—and unsure how to actually build something useful with Python? You’re not alone. The AI landscape moves fast, models evolve monthly, and most tutorials only teach you to copy code you’ll outgrow in a week. This book cuts through the noise and teaches you future-proof generative AI skills that will still matter as the ecosystem keeps shifting. You’ll learn the fundamentals that don’t go out of style (tokenization, embeddings, prompt engineering, transformers, diffusion, and fine-tuning) and see exactly how they translate into practical Python workflows. Build text, image, and code generators using modern libraries like PyTorch, Hugging Face, and LangChain. Understand not only how to use these tools, but why they work, so you can adapt your code as models and frameworks continue to evolve. From building RAG applications with your own data to evaluating model outputs and deploying them responsibly, you’ll gain the skills to design real, production-ready AI systems instead of relying on black-box APIs. Whether you're a Python developer, data scientist, or ML engineer expanding into generative AI, this book gives you the foundation and flexibility to stay ahead in a rapidly changing field. What You’ll Learn Core concepts behind LLMs, transformers, embeddings, and diffusion models - How to generate text, images, and code using modern Python libraries - Prompt engineering techniques that dramatically improve output quality - How to fine-tune models for your use case, including instruction tuning - Build retrieval-augmented generation (RAG) apps with your own data - Evaluation techniques to measure and improve AI output - Deployment strategies for scalable and secure AI applications - How to design AI workflows that remain adaptable as models and tools evolve Bert Gollnick is a senior data scientist who specializes in renewable energies. For many years, he has taught courses about data science and machine learning, and more recently, about generative AI and natural language processing. Bert studied aeronautics at the Technical University of Berlin and economics at the University of Hagen. His main areas of interest are machine learning and data science.