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

Course details

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.

Concepts

0. Introduction

01 - Intro to RAG with Azure AI
02 - Course prerequisites

1. RAG Core Concepts

03 - Fundamental concepts of RAG
04 - RAG process and architecture
05 - Vector databases
06 - Azure OpenAI embeddings model
07 - Creating a RAG solution with Azure AI Foundry

2. Azure AI Search

08 - RAG using Azure AI Search
09 - Preparing your resources for RAG
10 - Creating a search index
11 - Creating a data source
12 - Creating a skillset and indexer
13 - Querying your data
14 - Azure AI Search - Import and vectorize data
15 - Sending query results to a language model
16 - Other approaches
17 - Challenge - Create a RAG solution using Azure AI Search
18 - Solution - Create a RAG solution using Azure AI Search

3. Azure Cosmos DB

19 - RAG using Azure Cosmos DB
20 - Creating Azure Cosmos DB resource
21 - Set up Azure Cosmos DB for RAG
22 - Challenge - Create a RAG solution using Azure Cosmos DB
23 - Solution - Create a RAG solution using Azure Cosmos DB

4. Evaluating RAG

24 - Evaluation metrics in generative AI
25 - Preparing your evaluation dataset
26 - Evaluate with the Azure AI Evaluation SDK
27 - Challenge - Evaluating a RAG application
28 - Solution - Evaluating a RAG application

Conclusion

29 - Summary and next steps

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

Authors

Ziggy Zulueta