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Keep shoppingArchitecting LLM Apps on Azure: RAG, Agents, and Real-World
4,620 students
Architecting LLM Apps on Azure: RAG, Agents, and Real-World
Master Generative AI & Enterprise Solutions with Azure OpenAI & AI Foundry
Created byAlireza Chegini
Last updated 9/2025
English
What you'll learn
- Understand the fundamentals of LLM Architecture, Retrieval-Augmented Generation (RAG) and its role in improving LLM reliability.
- Learn the core Azure services used in RAG solutions, including AI Search, OpenAI, AI Studio, and Copilot Studio.
- Get to know most common LLM based reference architecture on Azure
- Design RAG application architectures using Azure AI Foundry no-code approach.
Explore related topics
Course content
7 sections • 19 lectures • 2h 45m total length
Requirements
- Basic level of AI or machine learning experience required
- Familiarity with basic cloud concepts is helpful (e.g. what a storage account or API is).
- An active Microsoft Azure account (free or paid) to follow along with hands-on lecture.
- Basic curiosity and willingness to learn about modern AI architecture and tooling.
Description
Architecting LLM Apps on Azure: RAG, Agents, and Real-World GenAI Solutions
This course gives you a practical, architecture-focused pathway to master Retrieval-Augmented Generation (RAG) and architect advanced LLM applications on Azure’s AI ecosystem. Whether you're a developer, architect, or product manager, this course helps you design context-aware AI systems that are secure, scalable, and enterprise-ready.
RAG ARCHITECTURE AS THE CORE AI PATTERN
Unlike general LLM courses, this program is laser-focused on Retrieval-Augmented Generation as a modern architecture pattern. You’ll understand:
Why RAG is essential to combat hallucinations
How it grounds responses using enterprise data
How to integrate Azure services like Azure AI Search, Azure OpenAI, and vector databases into the pipeline
FROM CONCEPTS TO PRODUCTION-READY DEPLOYMENT
We begin with the fundamentals of LLMs—what they are good at, where they fail, and how RAG bridges the gap. But this course goes much further.
You will learn:
Key LLM application architecture concepts on Azure
The differences between LLM apps and RAG solutions
How to extend LLM apps into agentic architectures by incorporating tools and dynamic data sources
CHOOSING THE RIGHT AZURE TOOLS: AI FOUNDRY VS. COPILOT STUDIO
A major highlight of the course is understanding when and how to use Azure’s no-code and low-code tools effectively:
Copilot Studio for business-led rapid prototyping
Azure AI Foundry for technical teams needing modular, configurable RAG/agent solutions
We explore when to choose each tool based on business needs, team skills, and deployment requirements.
LLM APPLICATIONS ARCHITECTURE ON AZURE – DEEP DIVE
We dive deep into Azure-based reference architectures, including:
Basic Azure AI Foundry chat reference architecture
Baseline Azure AI Foundry reference within Azure Landing Zone
Detailed breakdown of two practical architectures:
Extract and Analyze Call Center Data
Automate PDF Forms Processing
These references equip you to reuse, adapt, and design your own LLM solutions with clarity and alignment to enterprise patterns.
LAB: BUILD A RAG SOLUTION ON AZURE AI FOUNDRY
Hands-on learning culminates in an applied lab:
Set up an AI Foundry project
Deploy a model and create an intelligent agent
Upload documents and build a retrieval layer
Add knowledge and review agent features
Who this course is for:
- Product managers, analysts, and business leaders looking to understand the LLM Architecture concept and how RAG enables trustworthy AI assistants.
- Cloud developers and solution architects who want to design reliable AI applications using LLMs.
- Technical teams evaluating Azure AI services like OpenAI, AI Search, and Copilot Studio for internal copilots.
- Anyone interested in building AI-powered chatbots that are grounded in real business documents and data—no prior AI experience required.