Generative AI Systems: Building Generative AI Across Every Modality

Building Generative AI Across Every Modality

Behind every generative AI system — the chatbots and image generators, the video, music, and voice models, the coding assistants — lies the same small set of ideas. Learn them once, and the entire field falls into place.

This is the rare book that covers all of generative AI, not one corner of it: sixty chapters across thirteen parts, from first principles to production systems to the ethics of the field.

Its foundation is a single unifying insight. Almost all generative AI runs on two engines — the autoregressive transformer and the diffusion model — wrapped in one repeatable recipe: choose a representation, apply an engine, condition it for control, and evaluate against the right bar. Master that pattern and every new model, however novel it looks, becomes a variation on a theme you already understand — an approach built to outlast the tools and product names that change every few months.

Inside, you'll build real understanding across every major modality:

- Foundations — what generation is, and the math and neural networks beneath it

- Text & Language — transformers, tokenization, fine-tuning, prompting, RAG, and agents

- Images & Visual Art — diffusion, latent space, and precise conditioning

- Video & Motion — temporal coherence and image-to-video

- Audio & Music — speech synthesis, music generation, and recognition

- Code & Software — coding agents and verifiable correctness

- Data & Knowledge — synthetic data, extraction, and knowledge graphs

- 3D & Design — meshes, neural fields, text-to-3D, and CAD

- Interactive & Games — procedural content, living NPCs, and world models

- Business & Productivity — automation, assistants, and measurable ROI

- Scientific & Technical — molecule, material, and hypothesis generation

- Systems & MLOps — serving, scaling, monitoring, and security

- Ethics, Safety & the Future — bias, provenance, alignment, and what comes next

Every chapter is richly illustrated with diagrams, tables, and worked examples that make each architecture visible at a glance, and the parts build on one another into one coherent, continuously numbered progression.

Written for developers, engineers, founders, machine-learning practitioners, and serious students who want to build generative AI — not just use it. Whether you're shipping your first model or architecting systems at scale, you'll come away with both the durable mental models and the practical engineering discipline to do it well.

The models will keep changing. These principles won't. Start building.