Making prompts longer did not help. Making the task contract explicit did (signaldepth.ai)

Recent findings from the Signal Depth benchmark reveal that simply extending prompt lengths does not enhance AI model performance when task contracts remain vague. Instead, the research highlights that explicitly defining the task contract leads to significant improvements in model accuracy. In tests conducted with various model configurations, adding clear input/output specifications increased average pass rates from 9% to a remarkable 95%. This challenges the common belief that longer prompts inherently lead to better model responses. The significance of this study lies in its ability to clarify misunderstanding surrounding prompt design in AI/ML. By isolating the impacts of prompt specificity from the effects of length, researchers can better diagnose failure modes in model performance. The current E15 findings focus on deterministic Python code tasks, underscoring that the implications are not universally applicable across all AI capabilities, such as multi-turn dialogues or document synthesis. This work not only deepens our understanding of model sensitivity to prompt design but also sets a new standard for how AI researchers should approach task definitions in future studies.