AI-3016: Develop Generative AI Apps in Azure AI Foundry Portal

Build and Launch Real-World Generative AI Solutions with Azure AI Studio

Unlock the power of generative AI with Microsoft's cutting-edge Azure AI Foundry Portal. This hands-on course teaches you how to design, build, and deploy intelligent applications using GPT-4, Prompt Flow, Retrieval-Augmented Generation (RAG), fine-tuning, and automated evaluation—all within the Azure AI Studio ecosystem.

What You’ll Learn

Navigate Azure AI Studio: Learn how to create and manage AI projects, model deployments, and prompt workflows in a secure enterprise-ready environment.
Deploy State-of-the-Art Models: Discover how to work with powerful foundation models like GPT-4, Llama 2, and Phi-2, and deploy them within your own Azure subscription.
Use Your Own Data (RAG): Enhance AI responses using your private data with Retrieval-Augmented Generation and data connectors.
Fine-Tune and Evaluate: Train your own variants of foundation models and compare their performance using Azure AI Studio’s built-in evaluation tools.
Ensure Responsible AI: Explore content filtering, safety policies, and model monitoring using Microsoft’s built-in responsible AI tools.
Accelerate with Prompt Flow: Build multi-step generative AI apps that combine prompts, tools, and logic using Prompt Flow visual pipelines.

Who Should Take This Course

Developers and AI engineers ready to bring generative AI into production
Data scientists interested in deploying and customizing foundation models
Technical managers evaluating Microsoft's AI stack for their organizations
Teams seeking a secure, scalable, and cost-effective way to integrate LLMs

Course Format

100% hands-on with step-by-step labs in your own Azure environment
Taught by a virtual AI instructor
No fluff—just real-world implementation guidance
Includes lab exercises on model deployment, prompt engineering, fine-tuning, data integration, and evaluation

Category: AI and Machine Learning
Level: Intermediate
Time Estimate: 10h 30m
Price: $99 for 3 months of access
Subscription: $39.99 per month after 7-day free trial
Lab Environment: Included
Free Trial: 7 Days

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Lessons in this Course

Lesson 1: Introduction to Azure AI Studio

Welcome to Lesson 1. In this lesson, we're going to explore Azure AI Studio—the central environment for developing, deploying, and managing generative AI projects in Azure. This portal consolidates capabilities for selecting models, configuring endpoints, managing resources, and building intelligent applications using prompt flow and model playgrounds.

Azure AI Studio is designed to meet the needs of both developers and business users. It integrates deeply with Azure services such as Azure AI Search, Azure OpenAI, and Azure Resource Manager, while offering a streamlined UI that supports experimentation and production workflows.

Duration: 1 h 30 m

Exercises

Lesson 1: Introduction to Azure AI Studio In this exercise, you use Azure AI Foundry portal to create a project, ready to build an AI solution. Estimated Time: 30 minutes

Lesson 2: Explore and Deploy Models from the Model Catalog

Welcome to Lesson 2. In this lesson, we’ll dive into the Azure AI Studio’s model catalog—a central repository for finding and deploying foundation models like GPT-4o, Phi-3-mini, and more.

Choosing the right model is essential for performance, cost, and alignment with your application needs. Azure AI Studio allows you to evaluate different models using benchmarking data across categories like fluency, reasoning, and latency.

Duration: 1 h 30 m

Exercises

Exercise 1: Choose and deploy a language model In this exercise, you'll explore the model catalog in Azure AI Foundry portal, and compare potential models for a generative AI application that assists in solving problems. Estimated Time: 25 minutes

Exercise 2: Create a generative AI chat app In this exercise, you use the Azure AI Foundry SDK to create a simple chat app that connects to a project and chats with a language model. Estimated Time: 40 minutes

Lesson 3: Use Prompt Flow to Develop Language Model Apps

Welcome to Lesson 3. Now that you’ve explored model selection and deployment, we’ll shift our focus to orchestrating interactions with those models using **Prompt Flow**.

Prompt Flow is a visual and code-based environment in Azure AI Studio that lets you define a pipeline of interactions. These pipelines can include system prompts, user inputs, chat history, and model responses, allowing you to build intelligent and context-aware language applications.

Duration: 1 h 30 m

Exercises

Exercise 1: Use a prompt flow to manage conversation in a chat app' In this exercise, you'll use Azure AI Foundry portal's prompt flow to create a custom chat app that uses a user prompt and chat history as inputs, and uses a GPT model from Azure OpenAI to generate an output. Estimated Time: 30 minutes

Lesson 4: Build a RAG-Based Copilot with Your Own Data

In this lesson, we introduce one of the most powerful techniques in generative AI—**Retrieval Augmented Generation (RAG)**. While language models are impressive, they do not have access to real-time or domain-specific data unless you give it to them.

RAG allows you to combine the capabilities of a large language model with external data sources. This is especially valuable when you want your AI assistant to provide up-to-date, verifiable, or business-specific responses.

Duration: 1 h 30 m

Exercises

Exercise 1: Create a generative AI app that uses your own data In this exercise, you'll use Azure AI Foundry to integrate custom data into a generative AI solution. Estimated Time: 45 minutes

Lesson 5: Integrate a Fine-Tuned Language Model with Your Copilot

Welcome to Lesson 5. Today we’re exploring how to **fine-tune a language model** in Azure AI Studio to meet your specific application needs.

Out-of-the-box foundation models are powerful but not always aligned with domain-specific tone, tasks, or vocabulary. Fine-tuning allows you to adapt a pre-trained model to your business context using your own data.

Duration: 1 h 30 m

Exercises

Exercise 1: Fine-tune a language model In this exercise, you'll fine-tune a language model with the Azure AI Foundry that you want to use for a custom chat application scenario. You'll compare the fine-tuned model with a base model to assess whether the fine-tuned model fits your needs better. Estimated Time: 60 minutes

Lesson 6: Evaluate the Performance of Your Custom Copilot

Welcome to Lesson 6. Now that you've built and possibly fine-tuned a copilot, it’s time to evaluate its performance. Evaluation ensures your AI assistant behaves as intended—reliably, responsibly, and with high-quality outputs.

Evaluation can be both **quantitative** (metrics like relevance and fluency) and **qualitative** (human review of tone or correctness). In this lesson, we’ll explore tools available in Azure AI Studio to support both.

Duration: 1 h 30 m

Exercises

Exercise 1: Apply content filters to prevent the output of harmful content In this exercise, you'll explore the effect of the default content filters in Azure AI Foundry. Estimated Time: 25 minutes

Lesson 7: Responsible Generative AI

Welcome to Lesson 7. In this final lesson, we’ll explore how to develop generative AI solutions responsibly. As AI systems become more powerful, it’s essential that we consider fairness, safety, transparency, and accountability throughout the development lifecycle.

Responsible AI is not just a compliance checklist—it’s about designing systems that align with ethical principles and user trust.

Duration: 1 h 30 m

Exercises

Exercise 1: Evaluate generative AI model performance In this exercise, you'll use manual and automated evaluations to assess the performance of a model in the Azure AI Foundry portal. Estimated Time: 30 minutes