Precision (also positive predictive value) is the ratio of True Positives ($TP$) to the total number of positive examples in the data (true positives and false positives, $TP + FP$). $\text{precision} = \frac{TP}{TP + FP}$. A model with a perfect precision evaluation means that the model is predicting all positive examples correctly (i.e. no false positives).