Azure for Developers: Retrieval-Augmented Generation (RAG) with Azure AI

سرفصل های دوره

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.

01 - Introduction

01 - Intro to RAG with Azure AI

02 - Course prerequisites

02 - 1. RAG Core Concepts

01 - Fundamental concepts of RAG

02 - RAG process and architecture

03 - Vector databases

04 - Azure OpenAI embeddings model

05 - Creating a RAG solution with Azure AI Foundry

03 - 2. Azure AI Search

01 - RAG using Azure AI Search

02 - Preparing your resources for RAG

03 - Creating a search index

04 - Creating a data source

05 - Creating a skillset and indexer

06 - Querying your data

07 - Azure AI Search Import and vectorize data

08 - Sending query results to a language model

09 - Other approaches

10 - Challenge Create a RAG solution using Azure AI Search

11 - Solution Create a RAG solution using Azure AI Search

04 - 3. Azure Cosmos DB

01 - RAG using Azure Cosmos DB

02 - Creating Azure Cosmos DB resource

03 - Set up Azure Cosmos DB for RAG

04 - Challenge Create a RAG solution using Azure Cosmos DB

05 - Solution Create a RAG solution using Azure Cosmos DB

05 - 4. Evaluating RAG

01 - Evaluation metrics in generative AI

02 - Preparing your evaluation dataset

03 - Evaluate with the Azure AI Evaluation SDK

04 - Challenge Evaluating a RAG application

05 - Solution Evaluating a RAG application

06 - Conclusion

01 - Summary and next steps

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تولید کننده: LinkedIn Learning (Lynda)