Azure for Developers: Retrieval-Augmented Generation (RAG) with Azure AI
Azure for Developers: Retrieval-Augmented Generation (RAG) with Azure AI
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 54m | 317 MB
Instructor: Ziggy Zulueta
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 54m | 317 MB
Instructor: Ziggy Zulueta
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.
Learning objectives
- Understand what RAG is and what the different components and steps are.
- Create a RAG application using Azure AI Search, Azure Cosmos DB and Azure OpenAI.
- Evaluate a RAG application using the Azure AI Evaluation SDK.
Azure for Developers: Retrieval-Augmented Generation (RAG) with Azure AI
