8 New Generative AI Books Reshaping AI Development in 2025

Drawing from Omar Sanseviero's extensive experience at Hugging Face and Google, this book unpacks how to harness advanced generative AI models like transformers and diffusion techniques. You’ll gain practical skills in fine-tuning models for text, image, audio, and music generation, with hands-on code examples and guided projects illustrating model customization and deployment. Chapters detail balancing pretrained versus custom models and demonstrate combining building blocks to innovate across creative domains. This resource suits developers and AI practitioners eager to build and adapt generative models with clarity on both theory and application.

What started as a need to bridge the gap between traditional Java development and cutting-edge AI led Banu Parasuraman to write this guide focused on integrating large language models and generative AI with the Spring Framework. You’ll learn how to apply AI concepts directly within your Java applications, from setting up Spring AI modules to addressing security and ethical concerns tied to AI workloads. The book walks you through evolving from basic AI and Spring fundamentals to advanced integration techniques, making it particularly useful if you’re an intermediate to advanced Java developer wanting to expand your skill set into AI-driven software. Chapters on optimizing performance and anticipating future AI trends provide practical foresight for developers aiming to stay ahead.

This tailored book explores the latest advances in generative AI for 2025, focusing on your individual background and interests. It examines emerging technologies, recent research breakthroughs, and evolving applications in generative models, providing a customized exploration that matches your skill level and goals. You’ll engage with cutting-edge topics ranging from novel architectures to innovative use cases, all presented to foster deep understanding and practical knowledge. By concentrating on your specific objectives and preferences, this personalized guide reveals how to navigate the rapidly changing landscape of generative AI. It encourages you to stay ahead of new discoveries through focused insights that resonate with your unique learning path and technical aspirations.

Mark Liu challenges the conventional wisdom that mastering generative AI requires deep prior expertise by guiding you through creating powerful AI models using PyTorch and Python you likely already have. You learn the nuts and bolts behind building diverse models—from transforming English to French, generating text like GPT-2, to crafting music and images—each chapter layering practical skills in GANs, Transformers, and diffusion models. Liu’s background as a finance professor with two decades coding experience brings clarity to complex topics, making this accessible for Python programmers with basic machine learning knowledge. If you want a hands-on, project-driven path into generative AI without getting lost in theory, this book fits your needs.

What if everything you knew about generative AI system design was rethought through the lens of real interview challenges? Ali Aminian and Hao Sheng draw on their deep experience to break down the complexity of building generative AI systems into a clear, structured approach. You’ll find a 7-step framework and more than 280 diagrams that clarify intricate concepts, alongside detailed case studies like Gmail Smart Compose and ChatGPT, illustrating practical system architectures. This book suits anyone preparing for high-level AI interviews or those wanting to grasp how generative AI systems are engineered in the wild, beyond theory.

Amit Bahree draws on nearly a decade of experience leading generative AI initiatives at Microsoft to present a grounded, pragmatic guide to integrating these technologies into business workflows. You’ll explore how to leverage tools like ChatGPT, Bard, and Copilot to streamline content creation and software development, with detailed explanations of architectures, prompt engineering, and multi-modal AI. The book also tackles practical challenges such as hallucinations and jailbreaks, offering insights into AI safety and ethics. If you're an enterprise architect or senior developer aiming to embed generative AI at scale, this book offers clear, experience-based strategies without the hype.

This tailored AI book explores the rapidly evolving landscape of generative AI developments projected for 2025 and beyond. It delves into emerging trends, recent discoveries, and future challenges in generative AI, focusing on the areas that match your specific interests and background. By examining cutting-edge concepts and advancements personalized to your goals, this book offers a unique opportunity to engage deeply with the technologies shaping the next wave of AI innovation. With its tailored content, it fosters a learning experience that keeps you informed and prepared for the dynamic changes ahead.

Drawing from her experience authoring accessible technology guides, Pam Baker unpacks the complexities of generative AI in a way that anyone can grasp. You learn not only which tools are best suited for varied tasks like writing, image creation, and audio production, but also how to craft prompts that yield useful and relevant results. The book breaks down how to integrate AI into everyday workflows to boost productivity without needing a technical background. Whether you want to enhance your professional output or experiment creatively, this guide offers a straightforward path to harnessing generative AI's capabilities effectively.

Jonathan M. Sterling approaches the daunting field of generative AI with a fresh perspective, breaking down complex ideas like GANs and autoregressive models into digestible lessons tailored for beginners. His background in computer science and hands-on AI experience shine through as he guides you from foundational concepts to applying AI tools such as TensorFlow and PyTorch. The inclusion of over 350 ChatGPT prompts and quizzes turns this book into a practical workshop, not just a manual. If you're looking to understand how generative AI works and how to leverage it professionally without getting lost in jargon, this book offers a clear, stepwise path to build your skills.

Anand Vemula brings a focused lens to generative AI, blending foundational theory with hands-on coding techniques that go beyond surface-level explanations. You’ll get a clear walkthrough of core models like VAEs and GANs, paired with practical exercises using TensorFlow and PyTorch, which means you’re not just reading—you’re building. The chapters on model optimization and deploying AI in production ground the book firmly in real-world applications, especially valuable if you’re aiming to integrate generative AI into healthcare, finance, or creative industries. If you want to deepen your technical skills and understand how generative AI reshapes content creation and problem-solving, this book will fit neatly into your learning plan.