Chapter 1: Technical fundamentals of GenAI: The first chapter is not just a regular introduction to GenAI but also a great overview of the main technical concepts related to the entire model lifecycle, including the steps and techniques that are usually far from regular users (e.g., pre- and post-training activities). Also, it serves as the initial high-level mapping of all relevant concepts to specific Microsoft-related and Microsoft Azure services, and it sets the level for the rest of the book with highly technical concepts, explained (hopefully!) in an accessible and easy-to-understand manner.

Chapter 2: Advanced AI with Microsoft Azure and others: The second chapter builds on the initial mapping of services, and goes deeper into the full list of relevant Microsoft Azure and other resources, such as Microsoft Copilot, Copilot Studio, Snowflake Cortex, Azure Databricks, NVIDIA on Azure, and GitHub Models. It also connects with important building blocks such as the endless list of LLM and AI agent frameworks, available vector databases, and web application frameworks. This chapter is certainly the most complete guide of all GenAI things on Azure and a good way for you to understand all the potential options for your AI developments.

Chapters 3 to 6: Mix of model adoption topics and agentic patterns: These four chapters are the perfect mix to understand the GenAI model choice criteria and the customization patterns for GenAI on Azure. The chapters cover topics from the RAG versus fine-tuning dilemma to the most recent AI agents (and multiagents) topics, including prompt engineering topics for different types of GenAI models.

Chapter 7: GenAIOps and MLOps in Azure: This chapter enters a very nascent topic, such as the denominated GenAIOps or LLMOps, which is the domain or discipline related to the productization and monitoring of GenAI-enabled systems. It also covers all relevant MLOps topics for non-GenAI systems. From a Microsoft Azure perspective, this part of the book highlights specific areas of the Microsoft Foundry, with relevant evaluation and performance monitoring topics that enable model validation at scale, in a programmatic way, before and after prod-level deployments.

Chapter 8: GenAI governance framework: When we talk about “governance,” we know that this encompasses multiple topics, including responsible AI (RAI), AI security and safety, compliance, and that mix of data and AI governance that is becoming so relevant for any GenAI adopter out there. Because of the nature of the topic, this chapter will include a mix of definitions and key concepts, along with the very specific, technical concepts related to Microsoft Azure and other Microsoft platforms such as Defender or Purview.

Chapter 9: Expert interviews: Because no content can replace the experience from the field, the last chapter will include real-world use cases, along with high-quality expert interviews that will complement your learning and reading experience. From adopter companies to key Microsoft experts, this is the best possible ending ever for this book.

Appendices A and B and the Glossary: As any other O’Reilly or similar technical books, we will include quick references to official documentation, services, architectures, and other reading materials that may complement your experience.

In summary, the mix of chapter topics covers everything you need to first start then advance your GenAI experience with Microsoft Azure. This includes everything from the field, as advanced as you can get it, and as updated as this crazy pace of innovation allows it.