When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Working with large language models using Azure

本课程是 Microsoft Generative AI Engineering 专业证书的一部分

位教师： Microsoft

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Apply prompt engineering techniques to improve Large Language Model responses
Build Retrieval-Augmented Generation (RAG) pipelines using Azure services
Fine-tune and customize LLMs for domain-specific AI applications
Develop and deploy generative AI applications using Azure AI Foundry

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累 Software Development 领域的专业知识

本课程是 Microsoft Generative AI Engineering 专业证书专项课程的一部分

在注册此课程时，您还会同时注册此专业证书。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
通过 Microsoft 获得可共享的职业证书

该课程共有4个模块

Learn how to build, customize, and deploy generative AI applications using Large Language Models (LLMs) and Microsoft Azure. This hands-on course introduces the practical techniques developers use to improve AI application performance, reliability, and business relevance.

You’ll begin by exploring how LLMs work, including their architecture, capabilities, and limitations. From there, you’ll apply prompt engineering strategies to improve model outputs and build more effective AI interactions. The course then introduces Retrieval-Augmented Generation (RAG) pipelines, teaching you how to connect LLMs with external data sources to deliver grounded, accurate responses. You’ll also learn how to customize models using fine-tuning techniques and evaluate when to use fine-tuning, RAG, or hybrid approaches for different business scenarios. In the final modules, you’ll build and deploy generative AI applications using Azure AI Foundry and Azure OpenAI services while learning deployment, monitoring, and cost management strategies. By the end of this course, you’ll have practical experience building AI-powered applications using modern Azure AI tools and workflows.

单元详情

This foundational module introduces the core concepts behind Large Language Models (LLMs). You will start by exploring the fundamental architecture that powers models like GPT (Generative Pre-trained Transformer) and learn how they process information and generate human-like text. The second half of the module is dedicated to prompt engineering, where you will learn and apply essential techniques—from basic commands to advanced strategies like few-shot learning and chain-of-thought—to effectively communicate with and control AI models to achieve desired outcomes. Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025. Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.

涵盖的内容

8个视频8篇阅读材料5个作业

8个视频总计34分钟

Introduction to Microsoft Generative AI engineering certification4分钟
Introduction to Working with Large Language Models using Azure course3分钟
Introduction to LLMs and prompt engineering3分钟
The impact of LLMs6分钟
A look inside an LLM: From prompt to response5分钟
Why Prompt Engineering Matters4分钟
Crafting effective prompts6分钟
Module 1 summary: LLM fundamentals and prompt engineering2分钟

8篇阅读材料总计95分钟

Course syllabus and recommended background5分钟
Overview of LLM interaction10分钟
Exploring LLM architecture15分钟
LLM fundamentals: From tokens to sequential models15分钟
The blueprint of modern LLMs: The transformer architecture15分钟
Insights from LLM interactions10分钟
Techniques in prompt engineering15分钟
Prompt engineering success strategies10分钟

5个作业总计180分钟

Module 1 Evaluation: Graded Quiz30分钟
Interacting with LLMs: Basics30分钟
LLM architecture: Practice Quiz30分钟
Creating successful prompts60分钟
Prompt engineering skills: Practice Quiz30分钟

This module focuses on one of the most powerful techniques for enhancing LLMs: Retrieval-Augmented Generation (RAG). You will learn how to ground models in external, private, or real-time data sources to provide more accurate and contextually relevant responses. You will start by building a basic RAG pipeline using Azure services and then progress to constructing and optimizing advanced systems with techniques like semantic ranking and sophisticated data chunking strategies. Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025. Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.

涵盖的内容

5个视频6篇阅读材料6个作业

5个视频总计24分钟

Introduction to RAG: Grounding AI with data5分钟
RAG pipelines explained6分钟
Data sources for RAG: Azure AI Search and the Marketplace6分钟
Advanced RAG configurations5分钟
Module 2 summary: Mastering RAG pipelines3分钟

6篇阅读材料总计70分钟

Understanding RAG frameworks15分钟
Introduction to RAG techniques10分钟
Reviewing your first RAG pipeline10分钟
Advanced RAG pipeline techniques15分钟
Effective RAG optimization strategies10分钟
Case study: Implementing advanced RAG in a corporate setting10分钟

6个作业总计215分钟

Module 2 evaluation: Graded Quiz30分钟
Exploring RAG pipelines30分钟
Basic RAG pipeline setup35分钟
RAG fundamentals: Practice Quiz30分钟
Optimizing RAG implementations60分钟
Advanced RAG skills evaluation: Practice Quiz30分钟

This module explores fine-tuning as a powerful method for customizing an LLM's core behavior, style, or knowledge for specialized tasks. You will learn the entire fine-tuning workflow, from preparing a high-quality dataset to launching the training job and evaluating the customized model's performance in Azure. Critically, you will learn to strategically decide when to use fine-tuning versus RAG—or a hybrid of both—to create highly effective, domain-specific AI solutions. Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025. Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.

涵盖的内容

4个视频7篇阅读材料6个作业

4个视频总计19分钟

The art of fine-tuning6分钟
A guided tour of Azure's fine-tuning interface5分钟
Integrating domain expertise into your application5分钟
Module 3 summary: Mastering customization with fine-tuning2分钟

7篇阅读材料总计70分钟

Fine-tuning techniques10分钟
Learnings from fine-tuning LLMs10分钟
Evaluating your custom fine-tuned model10分钟
Strategies for domain integration10分钟
A framework for evaluating custom models10分钟
Analyzing domain specific LLMs10分钟
RAG vs. fine-tuning: A strategic decision framework10分钟

6个作业总计215分钟

Module 3 evaluation: Graded Quiz30分钟
Fine-tuning practice30分钟
Customized LLM implementation35分钟
Fine-tuning comprehension: Practice Quiz30分钟
From customization to application: A domain-specific LLM lab60分钟
Real world use assessment: Practice Quiz30分钟

This module transitions from theory to practice by guiding you through the end-to-end process of building and deploying a complete generative AI application. You will learn to design an application's architecture and user flow before using Azure AI Foundry and Prompt flow tools to build it. The module then covers the critical MLOps lifecycle, teaching you how to deploy your application as a secure endpoint, manage it in a production environment, and implement monitoring with Azure Monitor for performance and cost. Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025. Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.