Prompt variation isn't just an engineering nuisance, it's a window into fundamental LLM limitations. When a model's accuracy drops from 95% to 75% due to minor rephrasing, we're not just seeing brittleness; we're potentially exposing data contamination, spurious correlations, and shallow pattern matching. This talk explores prompt variation as a powerful diagnostic tool for understanding LLM reliability. We discuss how small changes in format, phrasing, or ordering can cause accuracy to collapse revealing about models memorizing benchmark patterns or learning superficial correlations rather than robust task representations. Drawing from academic and industry research, you will learn to distinguish between LLM's true capability and memorization, identify when models are pattern-matching rather than reasoning, and build evaluation frameworks that expose these vulnerabilities before deployment.