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

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

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

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

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

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

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