Question

Your team develops a RAG-based chat application in Azure AI Foundry. You maintain separate development, staging, and production Foundry projects. The lead wants automated promotion of validated changes through these environments using Azure DevOps, with the same evaluation suite executed as a quality gate before any change reaches production. Which approach best integrates the Foundry project lifecycle into a CI/CD pipeline while enforcing this gate?

Accepted Answer

Store project, prompt, and configuration assets in a Git repository, and configure a pipeline that deploys to staging, runs the Foundry evaluation suite as a gate, and only promotes to production if evaluation metrics meet defined thresholds

Answer

Manually export the project assets from development and re-import them into production through the Foundry portal after a developer reviews the changes

Answer

Enable autoscale on the production endpoint so that new model versions are automatically rolled out as traffic increases

Answer

Use a single shared Foundry project for all environments and rely on deployment slots to separate development and production traffic