RAG with Azure OpenAI (AI Search, CosmosDB, GraphRAG & more)

Name: RAG with Azure OpenAI (AI Search, CosmosDB, GraphRAG & more)
Rating: 4.5 (308 reviews)

Work with Azure AI Search, CosmosDB, Neo4js, Azure OpenAI and Content Understanding with Multi-Modal RAG

Created byKuljot Singh Bakshi

Last updated 2/2026

English

What you'll learn

RAG (Retrieval Augmented Generation): Using Private Data with LLM
RAG with Azure AI Search
RAG with Azure CosmosDB
RAG with Azure OpenAI Studio
RAG with Azure AI Studio
RAG with Microsoft Copilot Studio
GraphRAG with Neo4j and Azure OpenAI

Course content

14 sections • 64 lectures • 13h 25m total length

Course Introduction4:55

Deploying Azure OpenAI Resource (Hands-On Lab)3:27
Deploy an Azure OpenAI resource in portal.azure.com, create a Sweden Central resource group, and configure primary and secondary keys and endpoints for bearer token API calls.
Chatting With Our Model (Hands-On Lab)13:08
Image Generation Model (Hands-On Lab)3:23
Note: UI Update for Azure OpenAI Resource!!!1:50
ChatCompletions API to call engine via code (Hands-On Lab)13:06
Making Use of Embedding engines (Hands-On Lab)7:48

Introduction to Prompt Engineering6:06
Master prompt engineering by crafting precise prompts to configure intelligent agents and guide outcomes, focusing on the four parts—goal, context, expectations, and source—for reliable results.
Prompt Engineering Techniques: CoT, Few-Shot etc.10:41
Explore prompt engineering techniques such as chain-of-thought prompting, zero-shot prompting, and few-shot prompting, plus best practices for configuring AI agents, structuring outputs, and breaking tasks into steps.

Introduction to Multi-Modal RAG with Azure AI Search15:10
Lab: Simple Image Verbalisation RAG (Infra Set-up: Hands-On Lab)26:46
Lab: Simple Image Verbalisation RAG (Running Python Code: Hands-On Lab)20:35
Lab: Advanced Doc Intelligence RAG (Infra Set-up: Hands-On Lab)16:34
Explore advanced multimodal rag for structured documents, preserving layout with the document intelligence layout API, and build an Azure search index using a multi-service account and invoices.
Lab: Advanced Doc Intelligence RAG (Running Python Code: Hands-On Lab)17:59

Introduction to Azure AI Content Understanding20:52
Learn how Azure AI content understanding extracts and labels unstructured data—from PDFs, images, audio, and video—via content and field extraction analyzers, to power RAG pipelines with vector databases.
Clarity on Pricing!2:36
Compare pro field extraction pricing with standard field extraction on Azure AI content understanding, and explain how large language model reasoning, verification, and redo cycles affect token costs.
Lab: Trying Out Content Understanding in Azure Portal (Hands-On Lab)13:23
Lab: Content Understanding with Python Notebooks (Hands-On Lab)16:19
Practice Azure AI content understanding in a code-first lab using Python notebooks to call the API, set endpoint and key, and run prebuilt analyzers for documents, images, audio, and video.
Lab: Creating and Testing Custom Analyzer (Hands-On Lab)20:02
Create a custom analyzer in Azure AI content understanding, define the schema and fields, test with invoices, then run analysis and call it via a Python notebook.
Introduction to Sustainability RAG Project Architecture4:03
Lab: Project Infrastructure Set-Up (Hands-On Lab)9:18
Set up Azure storage and blob storage, create Azure AI vector index, upload MP3, MP4, PNG, PDF, and deploy GPT-4 chat and text embedding 002 in the AI Foundry project.
Lab: Data Preparation for RAG (Hands-On Lab)32:31
Lab: Building and Running the RAG Pipeline (Hands-On Lab)14:06
Execute a hands-on rag pipeline that retrieves relevant content from Azure AI Search via vector embeddings and similarity search, then generate a response with an LLM.

Introduction to RAG With Azure CosmosDB20:28
Explore how Azure Cosmos DB supports RAG by providing globally distributed, low-latency NoSQL storage with multi-api support, vector search, and scalable throughput.
RAG with CosmosDB for NoSQL API: Architecture6:03
RAG with CosmosDB for NoSQL API: Demo (Hands-On Lab)29:19
Architecture for RAG with MongoDB Vcore6:30
RAG with MongoDB VCore Python Lab (Hands-On Lab)26:25

Requirements

Familiarity with Python Programming Language
Some experience working with Azure and Microsoft Cloud in General
General Software Engineering and development experience

Description

Unlock the power of Retrieval-Augmented Generation (RAG) with Azure OpenAI in this comprehensive Udemy course, designed for data professionals and AI enthusiasts eager to deepen their expertise in advanced AI techniques. This course provides hands-on insights into integrating RAG with various Azure services, enabling you to enhance knowledge retrieval within AI solutions.

Learning Objectives:

Master RAG with Azure AI Search:
Learn how to enhance search capabilities and retrieve relevant data effectively within Azure’s robust AI Search environment.
Implement RAG with Azure CosmosDB:
Discover methods to store, manage, and query large datasets, optimizing information retrieval for high-performance applications.
Leverage RAG in Azure AI Studio:
Utilize RAG within Azure AI Studio to develop custom solutions in a powerful development environment with enhanced flexibility.
Develop with RAG on Microsoft Copilot Studio:
Gain skills to create intelligent applications using Microsoft Copilot Studio, making user interactions more engaging and insightful.
Navigate Graph RAG with Neo4j:
Understand graph-based data storage and retrieval, allowing you to analyze complex relationships within data through Neo4j.

This course is packed with real-world applications, practical demonstrations, and code walkthroughs, equipping you with the knowledge to implement RAG across various Azure platforms and elevate your AI projects. Join now to gain in-demand skills and take your AI capabilities to the next level with RAG and Azure OpenAI!

Who this course is for:

Curious Developers looking to build cool apps with Azure OpenAI and GenAI
Senior Software Engineers looking for GenAI application
Any curious person in general looking to skill-up and keep-up with the industry!

RAG with Azure OpenAI (AI Search, CosmosDB, GraphRAG & more)

What you'll learn

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Course content

Introduction1 lecture • 5min

GenAI Primer4 lectures • 24min

Introduction to RAG3 lectures • 36min

Azure OpenAI Basics6 lectures • 43min

Prompt Engineering2 lectures • 17min

RAG With Azure AI Search3 lectures • 59min

Multi-Modal RAG With Azure AI Search5 lectures • 1hr 37min

Azure AI Content Understanding9 lectures • 2hr 13min

RAG with Azure CosmosDB5 lectures • 1hr 29min

Azure Icon Search Engine Project: Power of Azure CosmosDB3 lectures • 53min

Requirements

Description

Who this course is for: