More detailed prompts did not yield better helmet classifiers: the most prescriptive tier dropped two LLMs to about 0.58 accuracy
ChatGPT, Claude, Gemini and Grok each generated PyTorch helmet-compliance training scripts from three prompt tiers of increasing specificity, run 20 times on 972 balanced images: ten of twelve configurations converged to a 0.957–0.967 accuracy plateau, but the most detailed tier left Gemini and Grok near 0.58, with scripts that ran yet failed to converge. A backbone-by-scheduler ablation traced the failure to the prescribed from-scratch multi-scale network, which collapsed under both schedulers while a pretrained ResNet18 converged under either.