Step-by-Step Guide to RAG with LLMs Using Azure AI Foundry скачать в хорошем качестве

Step-by-Step Guide to RAG with LLMs Using Azure AI Foundry

This is a video about RAG with LLMs using Azure AI Foundry. In this episode, we dive into the integration of Retrieval Augmented Generation (RAG) with Large Language Models (LLMs) using Azure AI Foundry. We'll explain what LLMs and RAG are, why they are important, and guide you through a practical example of setting up a RAG system. Follow along as we create a resource group, set up a storage account, and deploy a model to perform semantic searches on a Netflix dataset. Finally, we'll demonstrate how to connect the AI model to generate movie recommendations solely based on the provided dataset. Join us for a detailed walkthrough and learn how to simplify AI implementations with Azure's powerful tools. 00:00 Introduction to LLMs and RAG 00:18 Understanding Large Language Models (LLMs) 00:38 Challenges with LLMs 01:13 Introduction to Retrieval-Augmented Generation (RAG) 01:34 Traditional LLM Workflow 01:57 Integrating RAG into LLM Workflow 02:48 Setting Up Azure for RAG 03:19 Creating a Storage Account and Uploading Data 07:00 Deploying Models and Creating Vector Embeddings 09:07 Creating Azure Search Service 12:41 Connecting RAG to Data Source and Testing 18:39 Python Code Explanation and Conclusion 🎥 Watch next:    • Build Your First AI Agentic RAG in Azure A...