This module focuses on one of the most powerful techniques for enhancing LLMs: Retrieval-Augmented Generation (RAG). You will learn how to ground models in external, private, or real-time data sources to provide more accurate and contextually relevant responses. You will start by building a basic RAG pipeline using Azure services and then progress to constructing and optimizing advanced systems with techniques like semantic ranking and sophisticated data chunking strategies.

This module explores fine-tuning as a powerful method for customizing an LLM's core behavior, style, or knowledge for specialized tasks. You will learn the entire fine-tuning workflow, from preparing a high-quality dataset to launching the training job and evaluating the customized model's performance in Azure. Critically, you will learn to strategically decide when to use fine-tuning versus RAG—or a hybrid of both—to create highly effective, domain-specific AI solutions.

This module transitions from theory to practice by guiding you through the end-to-end process of building and deploying a complete generative AI application. You will learn to design an application's architecture and user flow before using Azure AI Foundry and Prompt flow tools to build it. The module then covers the critical MLOps lifecycle, teaching you how to deploy your application as a secure endpoint, manage it in a production environment, and implement monitoring with Azure Monitor for performance and cost.