Evaluating Fine-Tuned Phi Models with Responsible AI in Mind

This article came after the fine-tuning guides because the workflow needed a real ending.

Training and deploying a custom model can feel like the finish line. It is not. Once a model has been adapted to a task, the next question is whether it behaves well enough for the environment where it will be used.

That means looking beyond accuracy or a successful deployment. Developers need to understand where the model fails, what kinds of responses it produces under pressure, and whether the system has been evaluated with safety and quality in mind.

That is why this post focused on evaluating fine-tuned Phi-3 and Phi-3.5 models in Azure AI Studio through a Responsible AI lens. The evaluation step turns fine-tuning from an experiment into something closer to an engineering workflow.

The reusable lesson is that model work has three parts: build, integrate, and evaluate. If evaluation is missing, the first two steps may be impressive, but they are not production-ready.

Read the original article:

Evaluate Fine-tuned Phi-3 / 3.5 Models in Azure AI Studio Focusing on Microsoft's Responsible AI