Fine-Tuning
AI Fundamentals
Fine-tuning is further training an existing AI model on targeted examples so it more consistently handles a specific task in a set style or format.
AI Fundamentals
What Is Model Distillation?
Model distillation is a technique for training a smaller AI model, called the student, to imitate the outputs of a larger, more capable model, called the teacher. The goal is a model that handles the teacher’s tasks, or a chosen subset of them, nearly as well while running faster and costing less to use. It is one reason AI providers can offer lighter, cheaper versions of their models alongside their flagship ones.
Also called distillation, knowledge distillation, AI distillation
Example
A hypothetical travel agency’s booking team needs the details (traveler names, dates, destinations, and requested changes) pulled out of every customer email so agents can act quickly. Running thousands of emails a day through the provider’s largest model works well, but it is slow and the cost adds up. The team tests a smaller, cheaper model from the same provider on a few hundred past emails whose details staff have already checked. Smaller models like this may be built partly through distillation, by training them to imitate a larger model’s answers.
The smaller model pulls details from routine emails almost as reliably as the large one and responds faster. The agency uses it for everyday messages and keeps the larger model for tricky ones, such as long threads that mix several requests. Staff still spot-check a sample each week.
How it works
Developers start by giving the teacher, the large model, a big set of example questions or tasks and recording its answers. The student, a much smaller model, is then trained to give the same answers, and in some setups to copy how confident the teacher was in each possible answer, not just its final pick.
Because the student is smaller, it needs less computing power every time it runs. That makes inference, the step where a trained model actually answers a request, faster and cheaper. The trade-off is breadth. A student usually matches the teacher best on the kinds of tasks it was trained on and can fall short on unfamiliar or complex ones. Distillation mainly copies behavior, so a student generally should not be expected to know more than its teacher.
Why you keep hearing about it
Most business readers meet the word in one of two places. The first is product menus. Many AI providers offer a family of models: a large flagship plus smaller versions described as faster or lighter. Distillation is one of the methods providers can use to build those smaller versions, though they do not always say exactly how a given model was made.
The second is the news. Distillation comes up in stories and disputes about one company training its AI model on the outputs of another company’s model. In those cases, the aim is usually to copy capabilities cheaply, and the resulting model is not necessarily a small one. Many providers’ terms of service restrict using their outputs to build competing models, so the practice can raise contract and intellectual property questions, not just technical ones. If your team ever plans to use one AI tool’s outputs to train another model, check the provider’s terms first.
What it means when you choose a model
The practical lesson is that the biggest model is not always the right one for a given job. Narrow, repetitive, high-volume tasks, such as sorting messages, tagging support tickets, pulling fields from forms, or summarizing short notes, often do not need the most capable model available. A smaller model may be good enough, tends to respond faster, and usually has a lower cost per token, a difference that matters most at high volume.
A sensible way to decide:
- Collect a sample of real examples where you already know the correct answer, your ground truth.
- Run them through a large model and a smaller one and compare the results side by side.
- Use the smaller model where it performs acceptably, and route harder or higher-stakes cases to the larger model or a person.
- Recheck quality from time to time, since the inputs you receive can change.
Distillation vs. fine-tuning and open-weight models
Distillation is easy to confuse with nearby terms. Fine-tuning means further training an existing model on your own examples so it gets better at your task or style. Distillation is about transferring what a big model can do into another model, usually a smaller, cheaper one: the student learns from the teacher’s answers so it can stand in for it at lower cost. The two can overlap, because one common way to distill is to fine-tune a small model on answers generated by a larger one.
Distillation is also separate from whether a model is an open-weight model. Open weight describes how a model is released (its trained settings are available to download and run yourself), not how it was built. A distilled model can be open-weight or available only through a provider’s service, and an open-weight model may or may not have been distilled.
Further reading
- AI ImplementationFor teams that want help redesigning a process, connecting systems, and deploying AI into everyday work.
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