A new position paper advocates for synthetic sequences generated from random processes to isolate how specific data traits drive model behavior. Current heuristics rely on compute-heavy experimentation with public datasets. This methodology aims to replace empirical guesswork with a principled framework. Practitioners can use these probes to optimize training and alignment stages.