The final step is the one you can feel directly. Once the prompt is a set of positions in meaning space, those positions tilt which outputs the model finds plausible. A bare prompt like "a cat" leaves a wide field open: any cat, any pose, any setting. Adding "on a skateboard" pulls the field toward a specific and unusual combination, and the model must reconcile two ideas that rarely appear together. Adding "wearing a tiny helmet, mid-air, city street at dusk" narrows it further, because each phrase removes whole regions of the space.
This is why wording matters more than length. A single precise word can do more than a paragraph of vague description, because it moves the prompt to a sharper region. It is also why contradictory detail fails: "a cat on a skateboard" and "a cat asleep in a cardboard box" pull in different directions, and the model has no coherent region to settle in, so it produces something that satisfies neither.
The simulation lets you test this directly. Start with the bare subject, then add one modifier at a time and watch how the spread of likely results contracts. The lesson is not that longer prompts are better, but that specific, mutually consistent detail is what turns a vague request into an instruction.