This course offers a deep dive into Retrieval Augmented Generation (RAG) using Azure AI Search, Azure Cosmos DB, and Azure OpenAI. It covers the definition, workings, and components of RAG, followed by detailed instructions on building a RAG solution with these Azure tools. Additionally, the course explains how to evaluate RAG applications using various performance metrics. Perfect for those looking to use Generative AI with their business data.
This is the repository for the LinkedIn Learning course Azure for Developers Retrieval Augmented Generation (RAG) with Azure AI. The full course is available from LinkedIn Learning.
In this course, Ziggy Zulueta—a Microsoft AI Most Valuable Professional and Certified Trainer—uses examples and practical applications to show you how to leverage Python with Azure Open AI, Cosmos DB, and AI Search to create cutting-edge Retrieval-Augmented Generation (RAG) solutions for enhanced data precision. Dive into RAG fundamentals, Python-based implementations, and performance evaluation methods. Learn how to set up Azure resources, create data indexes, apply skill sets for data enhancement, and automate the indexing process. Explore the importance of vector databases, tokenization, embeddings, and how they facilitate effective data retrieval and augmentation. Evaluate your RAG solutions to ensure accuracy, relevance, and safety. By the end of this course, you will be equipped to develop sophisticated RAG solutions that deliver precise and relevant insights tailored to your business needs.
The files are named to correspond to the videos in the course. The naming convention is CHAPTER#_MOVIE#. As an example, the branch named 01_05 corresponds to the first chapter and the fifth video in that chapter.
Data folder contains the data sets used in the course.
Ziggy Zulueta
Microsoft AI Most Valuable Professional
Microsoft Certified Trainer
Check out my other courses on LinkedIn Learning.