Fine-Tuning

Fine-tuning continues training a pretrained model on a narrower dataset to specialize behavior for a domain or task.

You take a general model and train it further on your own examples so it fits your use case.

Fine-tuning continues gradient updates on a pretrained model using domain or task data. Variants include full fine-tuning and parameter-efficient methods (e.g., LoRA).

Teams choose fine-tuning when prompting alone cannot meet style, format, or specialized behavior requirements.

• Domain language adaptation
• Structured output habits
• Specialized classifiers built on foundation models

• Improves task fit
• Can reduce prompt complexity
• Enables smaller specialized models via distillation afterward

• Needs quality labeled or preference data
• Risk of overfitting and regression on general skills
• Operational cost of managing versions

• Fine-tuning does not automatically insert private facts safely—prefer RAG for changing knowledge

Fine-tuning vs RAG?

Use RAG for updatable knowledge; fine-tune for behavior and format. Many production systems combine them.

• NIST AI RMF

• Howard & Ruder — ULMFiT (transfer learning for NLP)