Develop generative AI apps in Azure

Generative Artificial Intelligence (AI) is becoming more accessible through comprehensive development platforms like Azure AI Foundry. Learn how to build generative AI applications that use language models to chat with your users.

Plan and prepare to develop AI solutions on AzureMicrosoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you'll use and creating an optimal working environment for your development team. Introduction What is AI? Azure AI services Azure AI Foundry Developer tools and SDKs Responsible AI Exercise - Prepare for an AI development project Module assessment Summary Choose and deploy models from the model catalog in Azure AI Foundry portalChoose the various language models that are available through the Azure AI Foundry's model catalog. Understand how to select, deploy, and test a model, and to improve its performance. Introduction Explore the model catalog Deploy a model to an endpoint Optimize model performance Exercise - Explore, deploy, and chat with language models Module assessment Summary Develop an AI app with the Azure AI Foundry SDKUse the Azure AI Foundry SDK to develop AI applications with Azure AI Foundry projects. Introduction What is the Azure AI Foundry SDK? Work with project connections Create a chat client Exercise - Create a generative AI chat app Module assessment Summary Get started with prompt flow to develop language model apps in the Azure AI FoundryLearn about how to use prompt flow to develop applications that leverage language models in the Azure AI Foundry. Introduction Understand the development lifecycle of a large language model (LLM) app Understand core components and explore flow types Explore connections and runtimes Explore variants and monitoring options Exercise - Get started with prompt flow Module assessment Summary Develop a RAG-based solution with your own data using Azure AI FoundryRetrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Azure AI Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions. Introduction Understand how to ground your language model Make your data searchable Create a RAG-based client application Implement RAG in a prompt flow Exercise - Create a generative AI app that uses your own data Module assessment Summary Fine-tune a language model with Azure AI FoundryTrain a base language model on a chat-completion task. The model catalog in Azure AI Foundry offers many open-source models that can be fine-tuned for your specific model behavior needs. Introduction Understand when to fine-tune a language model Prepare your data to fine-tune a chat completion model Explore fine-tuning language models in Azure AI Foundry portal Exercise - Fine-tune a language model Module assessment Summary Implement a responsible generative AI solution in Azure AI FoundryGenerative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation. Introduction Plan a responsible generative AI solution Map potential harms Measure potential harms Mitigate potential harms Manage a responsible generative AI solution Exercise - Apply content filters to prevent the output of harmful content Module assessment Summary Evaluate generative AI performance in Azure AI Foundry portalEvaluating copilots is essential to ensure your generative AI applications meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your generative AI applications using the tools and features available in the Azure AI Studio. Introduction Assess the model performance Manually evaluate the performance of a model Automated evaluations Exercise - Evaluate generative AI model performance Module assessment Summary