5. The Dev Workflow¶
In the previous section, we saw the GenAIOps lifecycle: Ideation, Evaluation, Operationalization. Let's map those stages into the developer workflow shown below. Explore the Learning Resources for deeper dives into the tools and responsible AI considerations involved.
Click on the tabs below to understand the task to be completed at each stage.
Setup the Azure infrastructure for the project. This includes creating the Azure AI project (resources, models) and support services (Azure CosmosDB, Azure AI Search, Azure Container Apps). By the end of this step, you should have created an Azure resource group.
This step is completed for you in instructor-led sessions.
Setup the development environment for your project. This involves forking the sample repo to your own profile, launching GitHub Codespaces to get a pre-built development environment and configure it to talk to your provisioned Azure infrastructure. By the end of this step, you should be ready to start the ideation step of development.
Go from first prompt to functional prototype. This involves creating a prompt template, configuring it to use a deployed chat model, then using a sample input to iterate on the prompt template design till a satisfactory response is returned. By the end of this step, you should have a Prompty asset and a Python application script for Contoso Chat.
Assess response quality with larger test dataset. This involves creating a test dataset, creating custom evalators (for quality metrics) and orchestrating an AI-assisted evaluation workflow to scores responses from our application before we can deploy to production. By the end of this step, you should be ready to take the prototype to production.
Deploy application to get a hosted API endpoint. This involves creating an API application server (using FastAPI), packaging it up in am Azure Container App, and deploying it to Azure using azd deploy. By the end of this step, you should have a hosted Contoso Chat AI endpoint, ready to integrate with frontend clients.