ai-fine-tuning
Fine-tune models on your data to maximize quality and cut costs. Use when prompt optimization hit a ceiling, you need domain specialization, you want cheaper models to match expensive ones, you heard fine-tuning will make us AI-native, you have 500+ training examples, or you need to train on proprietary data. Also use when you have spent weeks of manual iteration with no systematic improvement path, or manual prompt tuning got you to a working system but quality plateaued. Covers DSPy BootstrapFinetune, BetterTogether, model distillation, and when to fine-tune vs optimize prompts, LoRA vs full fine-tune, when to fine-tune vs few-shot, distill GPT-4 into a smaller model, teacher-student model training, custom model training with DSPy, model distillation, make a cheap model as good as GPT-4.
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