Azure RAG Patterns: Azure AI Search & OpenAI Pipelines
Published 9/2026
Created by ACHRAF ER-RAYA
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 6 Lectures ( 1h 21m ) | Size: 951.8 MB
Master Retrieval Augmented Generation (RAG) on Azure: AI Search, Azure OpenAI, hybrid retrieval & agentic chat.
What you'll learn
⚡ Build automated Python ingestion pipelines to parse, chunk, & index documents with Hybrid Search & Semantic Ranker.
⚡ Design production RAG topologies using Azure AI Foundry SDK, query rewriting, & conversation state management.
⚡ Deploy safe RAG apps to Azure App Service with Azure Content Safety guardrails & automated RAG Triad evaluations.
⚡ Implement end-to-end security, Azure AI Content Safety guardrails, and automated RAG Triad evaluations to safely deploy production RAG apps to Azure.
Requirements
❗ Basic proficiency in Python and an active Azure subscription with access to Azure OpenAI services.
Description
This course contains the use of artificial intelligence.
Build a production-minded Retrieval Augmented Generation application on Azure instead of stopping at a toy chatbot.
This hands-on course takes you from Azure resource foundation to secure document ingestion, hybrid retrieval, cited chat, evaluation, and release review. You will use Microsoft Foundry, Azure AI Search, Azure OpenAI models, managed identity concepts, and practical engineering templates that you can adapt to an internal knowledge assistant.
Who this course is for
⭐ Python developers, AI engineers, and cloud architects looking to build secure, production-ready enterprise RAG applications on Azure.
Azure RAG Patterns: Azure AI Search & OpenAI Pipelines
