Develop a RAG-based solution with your own data using Azure AI Foundry

Module
8 Units

Intermediate

Data Scientist

AI Engineer

Azure

Azure AI services

Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Azure AI Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.

Learning objectives

By the end of this module, you'll be able to:

Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
Index your data with Azure AI Search to make it searchable for language models
Build an agent using RAG on your own data in the Azure AI Foundry portal

Prerequisites

Before starting this module, you should be familiar with fundamental AI concepts and services in Azure.

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Introduction min
Understand how to ground your language model min
Make your data searchable min
Create a RAG-based client application min
Implement RAG in a prompt flow min
Exercise - Create a generative AI app that uses your own data min
Module assessment min
Summary min