Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem

Large language models consistently fail the "car wash problem, " a viral reasoning benchmark requiring implicit physical constraint inference. We present a variable isolation study (n=20 per condition, 6 conditions, 120 total trials) examining which prompt architecture layers in a production system enable correct reasoning. Using Claude 3. 5 Sonnet with controlled hyperparameters (temperature 0. 7, topₚ 1. 0), we find that the STAR (Situation-Task-Action-Result) reasoning framework alone raises accuracy from 0% to 85% (p=0. 001, Fisher's exact test, odds ratio 13. 22). Adding user profile context via vector database retrieval provides a further 10 percentage point gain, while RAG context contributes an additional 5 percentage points, achieving 100% accuracy in the full-stack condition. These results suggest that structured reasoning scaffolds -- specifically, forced goal articulation before inference -- matter substantially more than context injection for implicit constraint reasoning tasks.