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The first step in shaping a base model into an assistant is to show it what a good answer looks like.
Humans write thousands of example conversations by hand. A question, then an ideal reply. Another question, another ideal reply. Each one written carefully to be helpful, honest, and clear. Together these examples become a new training set, far smaller than the internet the model first learned from, but pointed at a specific target.
Then the model trains on them the exact same way it always has. Look at the input, guess the output, measure how wrong it was, nudge the weights. The same guess-and-adjust loop, the same machine. The only thing that has changed is the diet. Now the inputs are questions and the targets are good answers.
After enough passes, the model has absorbed a new habit. Not "continue whatever text came before me" but "respond to this person helpfully." This is fine-tuning, and we have met it before: it is the same trick from the transformer module, where one pretrained model was cheaply re-pointed at a specific job. Here the specific job is simply being a good assistant.
Fine-tuning on its own gets you most of the way. The assistants people actually use usually add one more step, and that step changes the tone of the whole system.