Fine-Tuning
Definition
A technique for retraining the weights of a pre-trained foundation model on a domain-specific dataset to improve performance on a narrower class of tasks. Variants range from full fine-tuning through parameter-efficient methods (LoRA, QLoRA) to instruction tuning and reinforcement learning from human feedback.
Noise — Signal
Fine-tuning is recommended as the default answer to "the model doesn't know our data". In most cases it is the worse choice. For up-to-date knowledge, RAG is faster, cheaper and more auditable; for behavioural steering, prompting plus few-shot is usually enough; fine-tuning justifies itself primarily for stable, tightly defined tasks with sufficiently high-quality training data and a clear evaluation strategy. Fine-tuning without these preconditions produces models that are more expensive than the original and slowly degrade in production.
The right question
Not: "Should we fine-tune the model on our data?" But: "What is the specific task, what training data and eval infrastructure do we have, and which of the alternatives — RAG, prompting, a smaller model with a better pipeline — have we examined before choosing fine-tuning?"