Course outline March 2024
| Learning Path | Module | Practice Lab |
|---|---|---|
| Learning Path 1: Get started with Azure OpenAI Service | - What is generative AI - Provision a resource - Deploying a model - Using Azure OpenAI studio | - Exercise: Get started with Azure OpenAI Service |
| Learning Path 2: Build natural language solutions Service | - Use the APIs - Integrate Azure OpenAI into your app | - Exercise: Integrate Azure OpenAI into your app |
| Learning Path 3: Apply prompt engineering Service | - Understand what prompt engineering is - Review considerations for different endpoints - Explore different techniques of prompt engineering | - Exercise: Utilize prompt engineering in your app |
| Learning Path 4: Generate code | - Review how to construct code from natural language - Complete code and assist in development - Improve code and fix bugs | - Exercise: Generate and improve code with Azure OpenAI Service |
| Learning Path 5: Generate images | - Explore DALL-E in the studio - Use the REST API for DALL-E models - Use language specific SDKs for generating images | - Exercise - Generate images with a DALL-E model |
| Learning Path 6: Use your own data | - How using your own data works - Use the REST API - Use language specific SDKs | - Exercise: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service |