AI Engineering
Building the Next Generation of Intelligent Systems — Developing Prompt Workflows, AI Agents, and LLM Architectures for Production
About this book
Anyone can get an answer out of an AI model. Far fewer can build a system that produces the right outcome reliably, at scale, in production. This is where AI engineering becomes integral, spanning three distinct disciplines: the prompts that direct a model, the agents that act on its reasoning, and the large language models that underpin it all.
This combined edition brings together three bestselling volumes: AI Prompt Engineering by Nelson Ming, Agentic AI Engineering by Hyun Erwin, and LLM Systems Engineering by Rob McKinsey. Across 23 chapters, the authors guide readers from first principles to production-ready systems. Read end to end, the collection traces the full arc of modern AI engineering: covering everything from a single well-formed prompt to training the models running underneath.
Whether you are learning the fundamentals of AI prompt engineering or training and adapting LLMs in professional environments, this book provides a structured and practical guide to understanding and designing AI systems that perform reliably across real-world workflows.
What you'll learn
- Foundational prompt design and reasoning techniques
- Image and video generation, and hybrid prompt systems
- Building and orchestrating single- and multi-agent architectures
- Training, fine-tuning, and continued pretraining of LLMs
Table of contents
- 1Book I — AI Prompt Engineering (8 chapters, 44 modules)
- 2Book II — Agentic AI Engineering (7 chapters, 43 modules)
- 3Book III — LLM Systems Engineering (8 chapters, 42 modules)
Who this book is for
This book is written for developers, AI architects, engineers, and enthusiasts who are interested in replicable, high-quality outcomes. It assumes comfort with general programming and basic ideas from linear algebra and probability. The material is presented at an intermediate to advanced level.