Large language models' inability to consistently process numerical data stems from standard subword tokenization methods, which fragment numbers and lose their positional and decimal structure. To address this, researchers have developed Triadic Suffix Tokenization (TST), a novel scheme that divides digits into three-digit groups, or triads, and appends a magnitude marker to each1. This approach enables models to better understand numerical relationships and perform arithmetic operations. By preserving the structure of numbers, TST has the potential to improve the accuracy of large language models in scientific and mathematical reasoning tasks. The introduction of TST marks a significant step forward in enhancing the numerical reasoning capabilities of AI systems. This development matters to practitioners because it can lead to more reliable and efficient AI models, which is crucial for applications where accuracy is paramount, such as financial forecasting and scientific research.