Llm Application Architecture On Azure
Published 7/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 39m | Size: 721 MB
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 ArchitectureThis 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, build, and deploy context-aware AI systems that are secure, scalable, and enterprise-ready.RAG ARCHITECTURE AS THE CORE AI PATTERNUnlike 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 hallucinationsHow it grounds responses using enterprise dataHow to integrate Azure services like Azure AI Search, Azure OpenAI, and vector databases into the pipelineFROM CONCEPTS TO PRODUCTION-READY DEPLOYMENTWe 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 AzureThe differences between LLM apps and RAG solutionsHow to extend LLM apps into agentic architectures by incorporating tools and dynamic data sourcesCHOOSING THE RIGHT AZURE TOOLS: AI FOUNDRY VS. COPILOT STUDIOA 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 prototypingAzure AI Foundry for technical teams needing modular, configurable RAG/agent solutionsWe explore when to choose each tool based on business needs, team skills, and deployment requirements.LLM APPLICATIONS ARCHITECTURE ON AZURE - DEEP DIVEWe dive deep into Azure-based reference architectures, including:Basic Azure AI Foundry chat reference architectureBaseline Azure AI Foundry reference within Azure Landing ZoneDetailed breakdown of two practical architectures:Extract and Analyze Call Center DataAutomate PDF Forms ProcessingThese 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 FOUNDRYHands-on learning culminates in an applied lab:Set up an AI Foundry projectDeploy a model and create an intelligent agentUpload documents and build a retrieval layerAdd 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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