Generative AI Design Patterns

Preface

If you’re an AI engineer building generative AI (GenAI) applications, you’ve likely experienced the frustrating gap between the ease of creating impressive prototypes and the complexity of deploying them reliably in production. While foundational models make it easy to build compelling demos, production systems demand solutions to fundamental challenges: hallucinations that compromise accuracy, inconsistent outputs that break downstream processes, knowledge gaps that limit enterprise applicability, and reliability issues that make systems unsuitable for critical applications.

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.

In this book, we cover the entire AI engineering workflow. After an introduction in Chapter 1 ...