Generative AI is rapidly transforming industries, creating exciting and high-paying career opportunities for 2025. As businesses integrate AI-driven solutions into their operations, there is a growing demand for professionals who are skilled in Generative AI Tools. By mastering these technologies, individuals can position themselves for success in a variety of fields, from content creation to complex data analysis. The rise of Generative AI presents a unique opportunity for professionals to not only contribute to innovation but also to secure top-tier job roles in the rapidly evolving tech landscape.
Platforms like Azure OpenAI Service and Azure AI Foundry are at the forefront of enabling users to build and deploy powerful AI models. These tools provide a streamlined approach to creating cutting-edge applications, allowing professionals to harness the power of generative AI for real-world projects. In this blog, we’ll guide you through hands-on labs and projects that offer practical experience with generative AI, preparing you with the skills and expertise needed to excel in high-demand careers in 2025.
Table of Contents:
1. Hands-On Labs for Azure Generative Ai
- Lab 1: Explore the components and tools of the Azure AI Foundry
- Lab 2: Explore, deploy, and compare the language models in the Azure AI Foundry
- Lab 3: Build custom copilots with the prompt flow in the Azure AI Foundry portal
- Lab 4: Use prompt flow for Named Entity Recognition (NER) in the Azure AI Foundry portal
- Lab 5: Create a generative AI app that uses your own data
- Lab 6: Fine-tune a language model for chat completion in the Azure AI Foundry
- Lab 7: Explore content filters to prevent the output of harmful content in Azure AI Foundry
- Lab 8: Evaluate the performance of your custom copilot in the Azure AI Foundry
- Lab 9: Build a custom copilot using code-first development tools
- Lab 10: Get started with Azure OpenAI Service
- Lab 11: Deploy a model in Azure OpenAI Studio
- Lab 12: Integrate Azure OpenAI into your app (Using REST API)
- Lab 13: Integrate Azure OpenAI into your app (Using SDK)
- Lab 14: Utilize prompt engineering in your application
- Lab 15: Generate and improve code with Azure OpenAI Service
- Lab 16: Prepare to develop an app in Visual Studio Code
- Lab 17: Validate C++ Code errors Using Azure AI Studio
- Lab 18: Generate images with a DALL-E model
- Lab 19: Add your data for RAG with Azure OpenAI Service
- Lab 20: Mitigating Image Bias with Effective Prompts azure ai
- Lab 21: Invoke Foundation Models for Text Generation
What You Need Before Starting with Generative AI Labs & Projects
Before diving into the setup process, there are a few prerequisites to ensure a smooth experience:
- Microsoft Account: A valid Microsoft account is necessary to register.
- Browser Compatibility: Use a modern browser like Microsoft Edge, Chrome, or Firefox for optimal performance.
Related Readings: How to Create a Microsoft Azure Free Trial Account
Hands-On Labs for Azure Generative Ai
Lab 1: Explore the components and tools of the Azure AI Foundry
In this lab, you will explore the main elements and resources of Azure AI Foundry, Microsoft’s integrated platform for creating, modifying, and overseeing AI agents and applications at scale, will be covered in this lab. Through a straightforward portal, uniform SDK, and APIs, Azure AI Foundry provides a comprehensive suite of AI tools and capabilities that speed up the production process by enabling enterprise-grade governance, secure data integration, and model customization.
Lab 2: Explore, deploy, and compare the language models in the Azure AI Foundry
In this lab, You will investigate, implement, and contrast several language models from Azure AI Foundry in this lab. A wide variety of AI models are available with Azure AI Foundry, including Hugging Face’s open-source alternatives, OpenAI’s GPT models, and Microsoft’s proprietary models. You will discover how to deploy models effectively, choose the best model for various use cases, and assess models’ performance using metrics like scalability, accuracy, and latency.
You will have practical experience working with large language models (LLMs) in Azure AI Foundry at the end of this lab, which will help you make wise choices while creating AI-powered apps.
Related Readings: What is a large language model (LLM)?
Lab 3: Build custom copilots with the prompt flow in the Azure AI Foundry portal
In this lab, you will use the Azure AI Foundry portal’s Prompt Flow functionality to create custom copilots. You can create, test, and improve AI-powered processes that combine language models with different data sources and APIs using Prompt Flow. Creating structured prompts, chaining several AI calls, and optimizing responses for particular tasks like code generation, document summarisation, and customer support are all skills you will acquire.
You will have practical experience developing and implementing AI copilots by the end of this lab, giving you the ability to design intelligent assistants that are customized to your company’s requirements.
Lab 4: Use prompt flow for Named Entity Recognition (NER) in the Azure AI Foundry portal
In this lab, you will perform Named Entity Recognition (NER) using Prompt Flow in the Azure AI Foundry interface. One important Natural Language Processing (NLP) method for locating and categorizing entities in text data, including names, dates, locations, and organizations, is called NER. You will design an organized workflow with Prompt Flow that effectively pulls pertinent entities, applies AI models, and processes text.
You will have practical experience creating AI-driven NER processes at the end of this lab, which will enable you to improve text-processing apps and automate information extraction.
Lab 5: Create a generative AI app that uses your own data
In this lab, you will be using Azure AI Foundry, you will develop a generative AI application using your own data in this lab. You will discover how to optimize AI outputs to meet particular business objectives, adjust replies, and combine pre-trained language models with custom datasets. You will investigate methods like retrieval-augmented generation (RAG) to improve AI replies with domain-specific knowledge using the resources provided by Azure AI Foundry.
By the end of this lab, you will have developed a fully functional generative AI application that uses your proprietary data to dynamically generate insights, allowing for more intelligent and contextually aware AI interactions.
Lab 6: Fine-tune a language model for chat completion in the Azure AI Foundry
In this lab, you will use Azure AI Foundry to refine a language model for chat completion. By fine-tuning pre-trained models to particular use cases, you may increase the accuracy and relevancy of AI-generated responses. In order to create more context-aware and domain-specific chat interactions, you will discover how to set up training data, adjust hyperparameters, and refine a model.
You will have practical experience modifying language models for chat applications at the end of this lab, giving you the tools you need to create conversational agents powered by AI that are perceptive and responsive.
Lab 7: Explore content filters to prevent the output of harmful content in Azure AI Foundry
In this lab, you will learn how to use Azure AI Foundry’s content filters in this lab to stop offensive or dangerous content from being created. In order to guarantee AI safety, compliance, and ethical AI deployment, content screening is essential. To identify and reduce biased, offensive, or dangerous outputs from generative AI models, you will discover how to set up and use built-in content moderation tools.
You will know how to put strong content-filtering techniques into practice by the end of this lab, guaranteeing that your AI applications produce responsible and reliable results.
Lab 8: Evaluate the performance of your custom copilot in the Azure AI Foundry
In this lab, you will assess your bespoke copilot’s Azure AI Foundry performance in this experiment. Developing dependable and effective copilots requires measuring and improving AI performance. Using integrated assessment tools, you will discover how to evaluate important metrics including response correctness, latency, and user happiness. You will also learn how to modify model parameters, enhance prompts, and increase copilot effectiveness in general.
You will have a methodical way to assess and improve your AI copilot at the end of this lab, making sure it provides excellent, contextually appropriate answers for practical uses.
Lab 9: Build a custom copilot using code-first development tools
In this lab, you will use Azure AI Foundry’s code-first development tools to create a custom copilot. The code-first strategy offers more flexibility and control over model behavior, integrations, and optimizations than low-code or no-code approaches. You will discover how to create, train, and implement an AI copilot that is suited to certain business requirements using SDKs, APIs, and custom scripts.
You will have practical experience creating a completely customized AI copilot at the end of this lab, using cutting-edge coding techniques to improve functionality and performance.
Lab 10: Get started with Azure OpenAI Service
In this lab, you’ll explore the Azure OpenAI Service, which provides access to advanced language models like GPT for various AI applications. You’ll learn how to set up and configure the Azure OpenAI Service, integrate it into your applications, and begin leveraging its capabilities for tasks such as natural language processing and data analysis.
Related readings: Create Azure OpenAI Service Resources using Console & CLI: Step-by-step Activity Guide
Lab 11: Deploy a model in Azure OpenAI Studio
In this lab, you’ll deploy various models using Azure OpenAI Studio, including GPT-3.5-turbo-16k, GPT-3.5-turbo, and DALL·E. Azure OpenAI Studio allows you to manage and deploy advanced language and image models, providing a robust platform for integrating AI capabilities into your applications.
Related readings: Deploying Foundation Models in Azure OpenAI Studio
Lab 12: Integrate Azure OpenAI into your app (Using REST API)
In this lab, you’ll learn how to integrate Azure OpenAI services into your application using REST APIs. This involves making API calls to leverage the capabilities of Azure OpenAI models, such as GPT and DALL·E, within your own software solutions.
A REST API (Representational State Transfer Application Programming Interface) is a web service that allows systems to communicate over the internet using standard HTTP methods like GET, POST, PUT, and DELETE. REST APIs are designed to be stateless, meaning each request from a client to the server must contain all the information needed to understand and process the request.
Lab 13: Integrate Azure OpenAI into your app (Using SDK)
In this lab, you’ll explore how to integrate Azure OpenAI services into your application using the provided SDKs. This approach simplifies interactions with Azure OpenAI models by providing pre-built functions and methods, allowing for more efficient and streamlined integration compared to raw REST API calls.
An SDK (Software Development Kit) is a collection of tools, libraries, documentation, and code samples provided by a software vendor to help developers create applications for specific platforms or services. SDKs typically include APIs (Application Programming Interfaces) that allow your application to interact with the underlying platform, as well as development tools like compilers, debuggers, and emulators.
Lab 14: Utilize prompt engineering in your application
In this lab, you’ll explore the concept of prompt engineering and its application in enhancing interactions with language models like those available through Azure OpenAI. Prompt engineering involves crafting effective prompts to guide the model’s responses and optimize its performance for specific tasks.
Lab 15: Generate and improve code with Azure OpenAI Service
In this lab, you’ll learn how to use the Azure OpenAI Service to generate and enhance code. Azure OpenAI models can assist in various coding tasks, from generating code snippets to improving existing code by providing suggestions and optimizations.
Lab 16: Prepare to develop an app in Visual Studio Code
In this lab, you’ll set up your development environment in Visual Studio Code (VS Code) for building an application. Visual Studio Code is a powerful, lightweight code editor that supports a wide range of programming languages and tools, making it ideal for various development tasks.
Lab 17: Validate C++ Code errors Using Azure AI Studio
In this lab, you’ll learn how to leverage Azure AI Studio to analyze and validate errors in C++ code effectively. By integrating AI-powered tools, the lab demonstrates how to detect issues, suggest fixes, and streamline debugging processes, making code review and error resolution faster and more efficient for developers.
Lab 18: Generate images with a DALL-E model
In this lab, you’ll learn how to use the DALL·E model within Azure OpenAI Service to generate images from textual descriptions. DALL·E is designed to create high-quality, diverse images based on the prompts you provide, allowing for creative and customized image generation.
Lab 19: Add your data for RAG with Azure OpenAI Service
In this lab, you’ll learn how to add and integrate your own data into the Retrieval-Augmented Generation (RAG) framework using Azure OpenAI Service. RAG combines the retrieval of relevant information from a knowledge base with the generative capabilities of language models to provide more accurate and contextually relevant responses.
Lab 20: Mitigating Image Bias with Effective Prompts azure ai
This lab focuses on using Azure AI tools to identify and mitigate image bias through the strategic use of effective prompts. By understanding bias detection and applying prompt engineering techniques, participants will learn to create more inclusive and unbiased AI-driven image generation workflows.
Lab 21: Invoke Foundation Models for Text Generation
This lab explores how to leverage foundation models in Azure AI to generate coherent and contextually relevant text. Participants will learn to utilize pre-trained models, fine-tune prompts, and handle real-world scenarios, enabling efficient and creative text generation for various applications.
2. Real-Time Projects
Project 1: Synthetic Data Generation with LLM
The “Synthetic Data Generation with LLM” project involves leveraging Large Language Models (LLMs) to create high-quality synthetic data for various use cases, such as training AI models, enhancing data diversity, and addressing data privacy concerns. This project demonstrates how to utilize LLMs effectively for generating scalable, realistic, and diverse datasets that can accelerate AI development and testing processes.
Project 2: Building RAG Application With Langchain
The “Building RAG Application with LangChain” project focuses on creating a Retrieval-Augmented Generation (RAG) application that combines LangChain’s capabilities with external data sources to provide accurate and context-aware AI-driven outputs. This project highlights the integration of advanced retrieval and generation techniques to enhance knowledge-based systems, making it ideal for applications like intelligent document search, chatbots, and AI-assisted research.
FAQs
Is Azure AI the same as ChatGPT?
No, Azure AI and ChatGPT are not the same, but they are related. Azure AI is a suite of artificial intelligence services offered by Microsoft, including tools for machine learning, computer vision, speech recognition, and natural language processing. One of its key components is the Azure OpenAI Service, which provides businesses and developers access to OpenAI’s models, including ChatGPT, GPT-4, and DALL·E. On the other hand, ChatGPT is a specific AI-powered chatbot developed by OpenAI that generates human-like text responses based on user input. While ChatGPT can be accessed directly via OpenAI’s platform, it is also available through Azure OpenAI Service, allowing enterprises to integrate it into their own applications with enhanced security, scalability, and compliance features. Essentially, Azure AI is a broad AI ecosystem, whereas ChatGPT is just one of the AI models that can be utilized within it.
How can customers access Azure AI Foundry?
Customers can explore Azure AI Foundry without authentication to experience its cutting-edge AI capabilities. When ready to utilize templates, tools, and the model catalog for building AI solutions, users are prompted to register or sign in with their Azure account. Currently, there's no extra charge for using Azure AI Foundry itself; however, when deploying solutions, you're billed for the Azure AI services, Azure Machine Learning, and other Azure resources used within Azure AI Foundry at their existing rates.
Does Azure AI Foundry support models other than ChatGPT?
Yes, Azure AI Foundry includes a robust and growing catalog of frontier and open-source models from providers like OpenAI, Hugging Face, and Meta. Users can apply these models to their data, compare models by task using open-source datasets, and evaluate model performance with their own test data to determine the best fit for their use case.
How does Azure AI Foundry handle prompt injection and ensure security against malicious code?
Prompt templates in Azure AI Foundry's prompt flow provide robust examples and instructions to help avoid prompt injection attacks in applications. Additionally, Azure AI Content Safety helps detect offensive or inappropriate content in text and images, and content moderation checks for jailbreaks to ensure security.
Next Task: Enhance Your Azure AI/ML Skills
Ready to elevate your Azure AI/ML expertise? Join our free class and gain hands-on experience with expert guidance.
Register Now: Free Azure AI/ML-Class
Take this opportunity to learn from industry experts and advance your AI career. Click the image below to enroll:
The post Master Generative AI on Azure with Hands-On Labs & Projects for 2025 appeared first on Cloud Training Program.