This book bridges that gap by providing 32 battle-tested design patterns that address the recurring problems you’ll encounter when building production-grade GenAI applications. These patterns aren’t theoretical constructs—they codify proven solutions that are often derived from cutting-edge research and refined by practitioners who have successfully deployed GenAI systems at scale.

Supervised machine learning (ML) involves training a problem-specific model on a large training dataset of example inputs and outputs—but GenAI applications rarely include a training phase. Instead, they commonly use general-purpose foundational models. This book is focused on design patterns for AI applications that are built on top of foundational models, such as Open AI’s GPT, Anthropic’s Claude, Google’s Gemini, or Meta’s Llama.

Who Is This Book For?

This book is for software engineers, data scientists, and enterprise architects who are building applications powered by GenAI foundational models. It captures proven solutions you can employ to solve the common challenges that arise when building GenAI applications and agents. Read it to learn how experts in the field are handling challenges such as hallucinations, nondeterministic answers, knowledge cutoffs, and the need to customize a model for your industry or enterprise. The age-old problems of software engineering have new solutions in this realm. For example, ways to meet latency and constrain costs include distillation, speculative decoding, prompt caching, and template generation.

Understanding the different patterns in this book requires different levels of background knowledge. For example, Chain of Thought (Pattern 13) requires no more than a knowledge of basic programming, Tool Calling (Pattern 21) requires an understanding of API design, and Dependency Injection (Pattern 19) requires some experience developing large-scale software. However, Content Optimization (Pattern 5) requires familiarity with statistics and ML, and Small Language Model (Pattern 24) requires an understanding of hardware optimization. We expect that 75% of the book can be read and understood by a junior software engineer or a third-year computer science student. The remainder will require specialized knowledge or experience.

AI engineering overlaps heavily with software engineering, data engineering, and ML—but in this book, we’ve limited our focus to core AI engineering. We encourage you to think of this book as a companion to the literature on patterns in related areas. Specifically, the book Machine Learning Design Patterns (O’Reilly), also co-authored by Valliappa Lakshmanan, covers proven solutions to recurring issues you’ll encounter when training a bespoke machine-learning model for a specific problem.

You’ll also likely find yourself working with both bespoke ML models and general-purpose foundational models, depending on the use case. In some situations, you might start with a foundational model but then find that edge cases require you to customize (or fine-tune) it for your problem. This book and Machine Learning Design Patterns are complementary and will help you work with both models, so we recommend that you read both.