- Published 12/15/2026
- 1st Edition
Senior Microsoft cloud architects Ovais Mehboob Ahmed Khan and Nikola Ivetic deliver a practical, hands-on guide to building and scaling intelligent applications on Azure with Azure OpenAI, RAG, Semantic Kernel, responsible AI, and production-ready monitoring!
Take a practical, end-to-end approach to building intelligent applications with Mastering Generative AI for Azure Developers: A guide to intelligent application development by Ovais Mehboob Ahmed Khan and Nikola Ivetic. Designed for software developers, cloud architects, and AI practitioners, this book bridges the gap between generative AI theory and production-ready implementation on Microsoft Azure. Readers will learn how to design, build, deploy, and manage AI-powered applications using Azure AI services, while applying modern techniques such as Retrieval-Augmented Generation (RAG), Semantic Kernel, and agentic workflows. Covering responsible AI, security, scalable deployment with Azure Kubernetes Service (AKS), and advanced monitoring and analytics, the book equips teams to deliver reliable, secure, and enterprise-ready generative AI solutions at scale.
By reading this book, you will:
- Apply core generative AI concepts (including transformer-based models) to real-world Azure application scenarios
- Use prompt engineering techniques to improve response quality, consistency, and user experience in AI features
- Build grounded AI solutions with Retrieval-Augmented Generation (RAG) and orchestration using Semantic Kernel
- Address responsible AI, ethical considerations, and security risks when designing and operating GenAI applications
- Deploy generative AI apps at scale on Azure using cloud-native patterns, including Azure Kubernetes Service (AKS)
- Implement APIs and disconnected containers to support edge, offline, and air-gapped environment requirements
- Plan for future trends and evolving platform capabilities so your Azure-based GenAI solutions stay resilient and relevant
About This Book
- For software developers, cloud architects, and AI practitioners who want to move beyond demos and build production-ready generative AI applications on Azure using practical patterns, real use cases, and hands-on implementation guidance
- For engineering leads and platform teams responsible for scaling AI solutions who need clear guidance on responsible AI, security and operational guardrails, cloud-native deployment with AKS, disconnected container scenarios for controlled environments, and ongoing monitoring and analytics for intelligent apps
Table of Contents
CHAPTER 1: Introduction to Generative AI
CHAPTER 2: Overview of Azure Services
CHAPTER 3: Prompt Engineering for Generative AI
CHAPTER 4: Building Generative AI Solutions with Retrieval-Augmented Generation and the Microsoft Agent Framework
CHAPTER 5: Addressing Ethical, Security, and Operational Challenges in Generative AI
CHAPTER 6: Fine-Tuning Generative AI Models
CHAPTER 7: Deploying Generative AI Applications
CHAPTER 8: Operating and Deploying Agentic Applications on Azure
CHAPTER 9: Operating and Optimizing Generative AI and Agentic Systems
CHAPTER 10: Patterns, Pitfalls, and the Future of Agentic AI on Azure
