Artificial intelligence output curation treats generation as the beginning of selection rather than the final result. The curator compares candidates, rejects visually impressive but irrelevant options and chooses material whose content and associations support the brief.
Curation can also include editing, sequencing and documenting why an output was retained. When generation is inexpensive and abundant, the quality of the final work increasingly depends on disciplined criteria and the willingness to discard weak options.
Why curate AI outputs instead of using the first result?
Generative systems produce uneven options, and the first polished result may contain unintended meaning or fail important requirements that comparison reveals.
What makes an AI output worth selecting?
The output should satisfy the brief, support the intended meaning, avoid harmful errors and fit coherently with the rest of the work.
