Model Optimization in Microsoft Foundry: Supervised Fine-Tuning

A walkthrough of supervised fine-tuning in Microsoft Azure AI Foundry using a retail chatbot scenario. Starting from basic prompt engineering, it demonstrates why fine-tuning is preferable to RAG when you need structured outputs, reduced token usage, and lower latency. The tutorial covers preparing training and validation datasets (~40 and 10 examples respectively), submitting a fine-tuning job, monitoring training metrics (loss and accuracy), deploying the fine-tuned model, and comparing outputs — showing token usage drop from ~500 to ~73 tokens with more structured, on-brand responses.