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Training
Fine-tuning, distillation, system prompts: how a model gets specialised.
3 · Articles on this topic
Fine-tuning: how to adapt an AI model to a specific domain
A language model starts out as a generalist: it knows a little about everything, but nothing about your industry, your language, your rules. Fine-tuning is the technique that retrains an existing model on targeted examples so it becomes more competent and more consistent in a specific domain. Understanding what it is, when it actually pays off, and how it differs from simply feeding the model more context is the way to avoid expecting from it what it cannot deliver, and to see why even a specialized model gains when it is set against different perspectives.
The System Prompt: What Really Steers an AI Model
When you write to an AI assistant, you never start from a blank page. Before your question arrives, the model has already been handed a set of instructions you never see. It is called the system prompt, and it is the layer that decides who the model is, how it writes, and what it can do. Grasp it, and you understand why two AIs answer the same question so differently.
Distillation: how a smaller, faster AI model is born
Behind an AI assistant that answers instantly on your phone there is often distillation: the technique that transfers what a large model knows to a smaller, lighter one. How it works, what you gain and what you leave behind, and why it points to a world of many models rather than a single one.
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