What AI Model Distillation Taught Me About Shipping Real Products
Large AI models are impressive, but they are not always the right tool. They can be slow, expensive to run at scale and far more general than a single product feature needs. In my work as an AI developer, the most useful technique I have learned for closing that gap is model distillation.
What AI distillation means
Distillation is the process of teaching a smaller model to perform one job the way a larger model would. The large model acts as a teacher. It produces examples and judgements for a specific task, and a compact student model is trained to reproduce that behaviour. The student will never know everything the teacher knows, but for the one task it was trained on it can be surprisingly close, while running many times faster and at a fraction of the cost.
Why small, task specific models matter
- Speed, because a small model can answer in milliseconds instead of seconds
- Cost, because running your own compact model avoids paying for every request to a large hosted model
- Control, because you own the model, the data and how it improves over time
- Privacy, because data can stay on your own servers
For a product that has to make thousands of quick decisions a day, those four points decide whether a feature is practical or not.
A real example in Havoc
Havoc places short videos from different perspectives on the same story side by side. Creators post freely without tagging a topic or a side, so the platform has to work out on its own which clips belong together. Asking a large model about every new upload would be slow and expensive. Instead, I am training a small model of our own that learns how to link opposing clips on the same story. It is built for one job, and it can run quickly on our own infrastructure.
Lessons from building it
The first lesson is that data quality matters more than model size. A small model trained on clean, well labelled examples beats a bigger one trained on noise. My background as a data analyst helps here, because so much of the work is cleaning, checking and modelling data before any training starts.
The second lesson is to measure the task, not the model. What matters is whether the right clips end up next to each other, so that is what I test against.
The third lesson is to start narrow. A model that does one thing well can be shipped, improved and trusted. A model that tries to do everything is much harder to get right.
Where this is heading
I expect more products to be powered by many small, specialised models rather than one giant one. Distillation, good data modelling and careful engineering are how we get there. I will keep sharing what I learn as the Havoc model improves.