This paper investigates how the specificity of natural language prompts influences zero-shot classification performance in modern vision language models (VLMs) under severe data scarcity. Using a curated 285 image subset of MS COCO containing three everyday postures (sitting, standing, and walking/running), we evaluate OpenCLIP, MetaCLIP2, and SigLIP alongside unimodal and pose-based baselines. We introduce a three tier prompt design, minimal labels, action cues, and compact geometric descriptions and systematically vary only the linguistic detail. Our results reveal a counterintuitive trend where simpler prompts consistently outperform more detailed ones, a phenomenon we term prompt overfitting. Grad-CAM attribution further shows that prompt specificity shifts attention between contextual and pose-relevant regions, explaining the model dependent behaviour. The study provides a controlled analysis of prompt granularity in low resource image based posture recognition, highlights the need for careful prompt design when labels are scarce.