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  • Book Overview & Buying Azure OpenAI Essentials
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Azure OpenAI Essentials

Azure OpenAI Essentials

By : Amit Mukherjee, Adithya Saladi
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Azure OpenAI Essentials

Azure OpenAI Essentials

By: Amit Mukherjee, Adithya Saladi

Overview of this book

Find out what makes Azure OpenAI a robust platform for building AI-driven solutions that can transform how businesses operate. Written by seasoned experts from Microsoft, this book will guide you in understanding Azure OpenAI from fundamentals through to advanced concepts and best practices. The book begins with an introduction to large language models (LLMs) and the Azure OpenAI Service, detailing how to access, use, and optimize its models. You'll learn how to design and implement AI-driven solutions, such as question-answering systems, contact center analytics, and GPT-powered search applications. Additionally, the chapters walk you through advanced concepts, including embeddings, fine-tuning models, prompt engineering, and building custom AI applications using LangChain and Semantic Kernel. You'll explore real-world use cases such as QnA systems, document summarizers, and SQLGPT for database querying, as well as gain insights into securing and operationalizing these solutions in enterprises. By the end of this book, you'll be ready to design, develop, and deploy scalable AI solutions, ensuring business success through intelligent automation and data-driven insights.
Table of Contents (19 chapters)
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Part 1: Foundations of Generative AI and Azure OpenAI
5
Part 2: Practical Applications of Azure OpenAI: Real-World Use Cases
13
Part 3: Mastering Governance, Operations, and AI Optimization with Azure OpenAI

Advanced Prompt Engineering

In the previous chapter, we covered essential aspects of operationalizing Azure OpenAI (AOAI), focusing on monitoring key metrics such as API call volume, latency, and token usage to optimize performance. We also discussed AOAI resource quotas, highlighting strategies for managing and allocating quotas effectively across resources. Additionally, the chapter introduced the concept of production throughput units (PTUs), a reserved instance crucial for handling production workloads. To build resilient, enterprise-level generative AI applications, we explored scaling AOAI using multiple endpoints along with high availability (HA) and disaster recovery (DR) strategies.

So far, we’ve explored various scenarios where generative AI can streamline workflows and looked at how to optimize models to enhance their performance and reliability. In this chapter, we’ll dive into prompt engineering—a critical skill that allows us to shape the behavior...

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