LLM application architecture on azure

Architect Real-World RAG & Agentic AI Solutions on Azure Using AI Search, OpenAI, Copilot Studio And AI Foundry

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 no-code approach.

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
MASTER LLM Application Architecture

This course gives you apractical, architecture-focused pathwayto master Retrieval-Augmented Generation (RAG) and architect advanced LLM applications onAzure’s AI ecosystem. Whether you're a developer, architect, or product manager, this course helps you design, build, and deploy 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 onRetrieval-Augmented Generationas 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 likeAzure AI Search, Azure OpenAI, andvector databasesinto 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:

KeyLLM application architecture conceptson Azure

Thedifferences between LLM apps and RAG solutions

How toextend LLM apps into agentic architecturesby 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 Studiofor business-led rapid prototyping

Azure AI Foundryfor technical teams needing modular, configurable RAG/agent solutions

We explorewhen to choose each toolbased 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 toreuse, adapt, and design your own LLM solutionswith 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 anAI Foundry project

Deploy a model andcreate an intelligent agent

Upload documents andbuild 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.
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