What is…
Fine-tuning
Extra training on a smaller, focused dataset to specialize a model's style, format or skill.
Fine-tuning takes an already-trained model and continues training it on curated examples — customer-support transcripts, legal drafting, a company's tone of voice. It changes the model's default behavior without starting over.
For most people, prompting and RAG come first: they're cheaper, faster and easier to update. Fine-tune when you need a consistent format or style at scale that prompts can't reliably produce.
💡 Think of it like
A general doctor doing a residency in dermatology.
🧠 Test yourself
Which of these describes Fine-tuning?
Related terms
LoRA (Low-Rank Adaptation)
A cheap fine-tuning method that trains a tiny add-on instead of the whole model.
Pre-training
The first, most expensive phase: teaching a model language by having it predict the next token across trillions of tokens.
RAG (Retrieval-Augmented Generation)
Looking up relevant documents first, then having the model answer using them — grounded, current, citable.
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