[100% Off] Architecting Llm Apps On Azure: Rag, Agents, And Real-World

Master Generative AI & Enterprise Solutions with Azure OpenAI & AI Foundry

Added on October 7, 2025 IT & Software 2 min read

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.

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