Output quality

    Prompt adherence

    Also called Instruction following, Prompt following.

    Prompt adherence is how faithfully a model does what the prompt actually said, as opposed to producing something attractive in the same neighbourhood.

    It is the axis on which models differ most and marketing says least. Two models can be equally capable of a beautiful frame while disagreeing completely about whether "holding the bottle in her left hand, label facing camera" is a requirement or a suggestion. For commercial work the second question is the one that costs money.

    The hard cases are consistent across the field: counting (three of something, not four), spatial relations (behind, to the left of, on top of), text rendering, negation, and multiple attributes bound to multiple subjects — the red hat on the woman and the blue hat on the man, kept that way round.

    Guidance settings raise adherence up to a point and then start distorting the image instead. Beyond that point the honest fix is either a different model or a different approach — reference images and masked edits pin down what a prompt keeps failing to.

    In practice

    • Test adherence with a brief that has a checkable requirement — a count, a position, a specific colour on a specific object.
    • Put the non-negotiable requirement early in the prompt.
    • If two attributes keep swapping between subjects, generate them separately and composite.

    The mistake to avoid

    Judging a model by its best output. Adherence is about the worst of five runs, because that is the one that shows what the model treats as optional.

    Related terms

    The all-in-one AI studio for creators. 60+ models for video, image, voice, music and lipsync in a single app.