Iteration refers to the repeated process of generating, evaluating, and refining outputs to progressively move toward a desired result. In AI generation contexts, iteration involves running multiple generations with adjusted prompts, parameters, or reference inputs, learning from each result to inform the next attempt.

Effective iteration requires understanding what aspects of a generation need improvement and which controls or modifications will move outputs in the desired direction. This might involve refining prompt language, adjusting technical parameters like guidance scale or sampling steps, changing reference images, or switching between models with different strengths. The iterative process is fundamental to achieving professional-quality AI-generated content, as first attempts rarely produce exactly the desired result.

In creative workflows, iteration is where the craft happens, where creators develop prompting skills, build intuition about how models respond to different inputs, and progressively refine outputs toward their vision. Platforms that support efficient iteration through features like variation generation, parameter adjustment, and prompt history enable more productive creative exploration than systems requiring full regeneration for each experiment.