Goal

Help the user fine-tune a model on Azure AI Foundry. This covers the full lifecycle:

1. Dataset creation — generate or prepare training data
2. Dataset evaluation — assess data quality before training
3. Base model evaluation — benchmark the un-tuned model
4. Training type selection — choose SFT, DPO, or RFT
5. Dataset format conversion — convert data to the right format
6. Training job submission — launch and monitor training runs
7. Training curve analysis — detect overfitting, pick checkpoints
8. Iterative experimentation — plan successive runs from results
9. Model deployment — deploy fine-tuned models with correct format/SKU
10. Model evaluation — score outputs with an LLM judge

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Workflow