What is Fine-Tuning?
The process of further training a pre-existing foundation model on domain-specific data to specialize its behavior, tone, or knowledge. Unlike prompting (which guides behavior at inference time) or RAG (which retrieves external knowledge), fine-tuning permanently alters the model's weights, baking new patterns into the model itself.
My perspective
In practice
Fine-tuning is powerful but often overkill. For most use cases, RAG plus good prompt engineering delivers 90% of the value at a fraction of the cost and complexity. Fine-tuning makes sense when you need consistent stylistic behavior (brand voice), domain-specific pattern recognition (medical coding, legal classification), or performance on tasks where prompting alone falls short. The key trade-off: fine-tuned models are expensive to create, hard to update, and lock you to a specific base model version.