AI-900 LAB: Retrieval-Augmented Generation (RAG) with Azure AI Foundry Part - 1
Retrieval-Augmented Generation (RAG) with Azure AI Foundry
Welcome to this hands-on AI-900 lab session, where we explore Retrieval-Augmented Generation (RAG) with Azure AI Foundry! RAG enhances AI models by combining pre-trained generative AI with real-time data retrieval, allowing for more accurate, context-aware, and up-to-date responses. Whether you're preparing for the Microsoft AI-900 Certification or looking to build highly intelligent AI applications, this step-by-step tutorial will help you understand and implement RAG in Azure AI Foundry.
🔍 What You’ll Learn in This Video:
1️⃣ Introduction to Retrieval-Augmented Generation (RAG) & Its Benefits
2️⃣ How RAG Improves AI Accuracy with Real-Time Data
3️⃣ Setting Up Azure AI Foundry for RAG Implementation
4️⃣ Integrating External Knowledge Sources with AI Models
5️⃣ Building a RAG Pipeline for AI-Driven Search & Question Answering
6️⃣ Optimizing & Deploying RAG-Enabled AI Models in Azure
🛠️ Who Is This For?
AI & ML Enthusiasts exploring advanced AI techniques
Developers & data scientists working with AI-powered search & chatbots
Professionals preparing for the Microsoft AI-900 Certification
Businesses looking to enhance AI-driven applications with real-time information retrieval
📌 Key Highlights:
✅ Hands-on demo of Retrieval-Augmented Generation (RAG) in Azure AI Foundry
✅ Enhancing AI models with external knowledge retrieval
✅ Optimizing AI for customer support, knowledge management, and enterprise search
✅ Deploying and testing RAG-powered AI applications
🔗 Watch Next
▶️ Next Lecture: • AI-900 LAB: Retrieval-Augmented Generation...
▶️ AI-900 Playlist: • AI-900 Azure AI Fundamentals
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💡 Learn how to integrate real-time knowledge with AI models using Azure AI Foundry & RAG today!
🔗 Additional Resources:
👉 Azure AI Foundry Documentation: https://microsoftlearning.github.io/m...
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Chapters:
0:00 - 01:07 - Intro
01:07 - 05:17 - What is RAG?
05:17 - 10:53 - Create an Azure AI Search resource
10:53 - 12:37 - Add data to your project
12:37 - 16:03 - Create an index for your data
16:03 - 19:30 - Test the index