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 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.

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.

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