Learn how to build, customize, and deploy generative AI applications using Large Language Models (LLMs) and Microsoft Azure. This hands-on course introduces the practical techniques developers use to improve AI application performance, reliability, and business relevance. You’ll begin by exploring how LLMs work, including their architecture, capabilities, and limitations. From there, you’ll apply prompt engineering strategies to improve model outputs and build more effective AI interactions. The course then introduces Retrieval-Augmented Generation (RAG) pipelines, teaching you how to connect LLMs with external data sources to deliver grounded, accurate responses. You’ll also learn how to customize models using fine-tuning techniques and evaluate when to use fine-tuning, RAG, or hybrid approaches for different business scenarios.
In the final modules, you’ll build and deploy generative AI applications using Azure AI Foundry and Azure OpenAI services while learning deployment, monitoring, and cost management strategies. By the end of this course, you’ll have practical experience building AI-powered applications using modern Azure AI tools and workflows.
