Foundational　segmentation　models,　predominantly　trained　on　scenes　typical　of　natural　environments,　struggle　to　generalize　across　varied　image　domains.　Traditional　＂training-to-adapt＇＇　methods　rely　heavily　on　extensive　data　retraining　and　model　architectures　modifications.　This　significantly　limits　the　models＇　generalization　capabilities　and　efficiency　in　deployment.　In　this　study,　we　propose　a　novel　adaptation　paradigm,　termed　＂prompting-to-adapt＇＇,　to　tackle　the　above　issue　by　introducing　an　innovative　image　prompter.　This　prompter　generates　domain-specific　prompts　through　few-shot　image-mask　pairs,　incorporating　diverse　image　processing　techniques　to　enhance　adaptability.　To　tackle　the　inherent　non-differentiability　of　image　prompts,　we　further　devise　an　information-estimation-based　gradient　descent　strategy　that　leverages　the　information　entropy　of　image　processing　combinations　to　optimize　the　prompter,　ensuring　effective　adaptation.　Through　extensive　experiments　across　nine　datasets　spanning　seven　image　domains　(i.e.,　depth,　thermal,　camouflage,　endoscopic,　ultrasound,　grayscale,　and　natural)　and　four　scenarios　(i.e.,　common　scenes,　camouflage　objects,　medical　images,　and　industrial　data),　we　demonstrate　that　our　approach　significant　improves　the　foundational　models＇　adaptation　capabilities.　Moreover,　the　interpretability　of　the　generated　prompts　provides　insightful　revelations　into　their　image　processing　mechanisms.　Source　code　is　available　at:　\urlgithub.com/yuema1303/Prompting-to-Adapt-FSM.　©　2024　ACM.