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AI Engineering: Building the Next Generation of Intelligent Systems – Developing Prompt Workflows, AI Agents, and LLM Architectures for Production
"Software 1.0 is code you write. Software 2.0 is neural nets. Software 3.0 is prompts."
— Andrej Karpathy, founding member of OpenAI
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 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 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 covering everything from a single well-formed prompt to training the models running underneath.
Main Topics
• 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
Features
• Personal insights from production environments
• Prompt and code examples
• 200+ visualizations and figures
• 350+ primary research citations
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.
Table of Contents
• Book I – AI Prompt Engineering (8 chapters, 44 modules)
• Book II – Agentic AI Engineering (7 chapters, 43 modules)
• Book III – LLM Systems Engineering (8 chapters, 42 ,modules)
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.
Scroll up and click Buy Now to start reading.
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 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 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 covering everything from a single well-formed prompt to training the models running underneath.
Main Topics
• 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
Features
• Personal insights from production environments
• Prompt and code examples
• 200+ visualizations and figures
• 350+ primary research citations
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.
Table of Contents
• Book I – AI Prompt Engineering (8 chapters, 44 modules)
• Book II – Agentic AI Engineering (7 chapters, 43 modules)
• Book III – LLM Systems Engineering (8 chapters, 42 ,modules)
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.
Scroll up and click Buy Now to start reading.
826 pages, Paperback
Published September 25, 2026
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