Practical prompting, backed by research

The paradox: a question placed before an image improves how the model 'sees' the picture — and simultaneously worsens the answer. This isn't random. It's a systemic problem across every vision model tested. Prompt Echoing lets you ask questions about images in a way that gets the model to both look at the picture correctly and hold onto the question by the time it answers. The trick: write the question twice — before the image and after. The first copy steers perception. The second one ends up right next to the answer — where the model actually reads.

Swap "option A" and "option B" but keep the same images. For one of the models tested, up to 60.9% of verdicts flip. Repeating the request in the same order changes only 1–10%. The method from the EditJudgeBias audit lets you tell whether you can trust an AI judge when it compares two photo-edit results. The trick: run the comparison in two mirrored orders and accept a winner only if both runs agree. That turns position bias (a pull toward the first or second slot) into a visible discrepancy. A sticker covering less than 4% of the frame moves the score more than the brightness or saturation of the whole image.