Concept · fine-tuning

Fine-tuning

Further training of a pre-trained model on a narrower dataset to specialize or steer its behavior.

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Settled

Broad technical consensus on what this term means. Disagreements that invoke it are about the world, not the word.

What it is

Fine-tuning takes a finished base model and continues training briefly on targeted data, examples of following instructions, a company's domain documents, a desired style. It is cheap relative to pre-training because the heavy general learning is already done.

It is the step that turns a raw next-token predictor into a usable product, and the standard lever for specialization. When a vendor offers "a model tuned on your data," fine-tuning is usually what is meant.

Understand first

foundation-modelFoundation model · A single large pre-trained model that serves as a general-purpose base for many downstream uses.

Builds toward

distillationDistillation · A training technique that teaches a smaller student model to mimic the outputs of a larger teacher model, producing compact models with disproportionately high capability.model-commoditizationModel commoditization · The process by which competitive pressure and open-weight alternatives erode the pricing power of any individual AI model or lab.open-weight-modelOpen-weight model · A model whose trained weights are publicly released, allowing anyone to run, fine-tune, or modify it without API access or per-token payment.rlhfRLHF · Reinforcement learning from human feedback: tuning a model toward outputs people rate highly.switching-costSwitching cost (AI API switching cost) · The friction, integration effort, prompt re-engineering, capability gaps, contractual lock-in, that makes replacing one model provider with another costly.