Researchers have recently proposed the GenSAM model, which achieves image segmentation through a universal task description, eliminating the reliance on sample-specific prompts. By employing the CCTP chain of thought and the PMG framework, experiments have demonstrated superior performance in camouflaged sample segmentation, with excellent generalization capabilities. The innovation of the study lies in providing a universal task description, making the model more efficient and scalable when processing large volumes of data. The introduction of GenSAM represents a significant step forward for prompt-based segmentation methods in practical applications, and it may potentially offer new insights and solutions for other fields in the future.