To improve a Large Language Model's performance on algebraic calculations, a few-shot Chain-of-Thought prompt can incorporate detailed problem-solving steps from a similar problem. For instance, a prompt can demonstrate the step-by-step calculation for the mean square of ${}1$, ${}3$, ${}5$, and ${}7$, which involves squaring each number (${}1^2 = 1$, ${}3^2=9$, ${}5^2=25$, ${}7^2=49$), summing the squares (${}1+9+25+49=84$), and dividing by the count (${}84 / 4 = 21$). By learning from this reasoning process, the model can successfully determine the average of ${}2$, ${}4$, and ${}9$ by computing the sum (${}2 + 4 + 9 = 15$), dividing by the count (${}15 / 3 = 5$), and arriving at the correct final answer of ${}5$.