Copilots are quickly becoming a popular way to use with AI models, automate tasks, and improve productivity. In this learning path, you'll explore options for adopting, using, and creating copilots.

This course is for Data Scientists and AI Engineers.

Before starting this module, you should be familiar with Azure and the Azure portal.

After completing this course, students will be able to:

• Understand generative AI's place in the development of artificial intelligence
• Understand language models and their role in intelligent applications
• Describe examples of copilots and good prompts
• Create and test copilots
• Analyze performance
• Describe core features and capabilities of Azure AI Studio
• Use Azure AI Studio to provision and manage an Azure AI resource
• Use Azure AI Studio to create and manage an AI project
• Understand when to use Azure AI Studio
• Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
• Index your data with Azure AI Search to make it searchable for language models
• Build a copilot using RAG on your own data in the Azure AI Studio

Fundamentals of Generative AI

• What is generative AI?
• What are language models?
• Using language models
• What are copilots?
• Microsoft Copilot
• Considerations for Copilot prompts
• Extending and developing copilots

Get started with Microsoft Copilot Studio

• Get started working with environments
• Create copilots and work with the Microsoft Copilot Studio interface
• Create topics
• Enhancing productivity with Generative AI
• Test your copilots
• Publish copilots and analyze performance

Introduction to Azure AI Studio

• What, how, and when to use Azure AI Studio

Build a RAG-based copilot solution with your own data using Azure AI Studio

• Understand how to ground your language model
• Make your data searchable
• Build a copilot with prompt flow