After writing the code-first Phi-3 guide, I wanted to explain the same workflow from another entry point.
Not every developer starts with scripts and local setup. Some people understand the system faster when they can see the Azure AI Studio and Azure ML Studio workflow: create the workspace, prepare compute, fine-tune, deploy, and connect the model to Prompt Flow.
This post helped me practice a different kind of teaching. The goal was not to simplify the engineering away. It was to expose the same architecture through a more guided interface.
That became an important lesson for me: a good technical explanation should meet developers where they start, then lead them toward the underlying system.
Read the original article:
Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow in Azure AI Studio