Introducing Classifiers with Jev

You can now use evaluation models in Mastra with a classifier. Define questions as choice, score, or boolean, then set the criteria to evaluate, and use the answer to make fast, predictable decisions.

With an evaluation model, you provide instructions describing what to decide and criteria outlining what can be answered. A choice returns one of the criteria keys, a score returns a position from an ordered list, and a boolean returns the probability the answer is true.

We shipped this after the dramatic rise of Jev a couple weeks ago, and ran a workshop showing how it works: Building a Classifier in Mastra with Jev.

Before classifiers, picking from a fixed set of options meant prompting an LLM, describing the options, defining the output shape, then parsing and validating whatever came back. With a classifier, the options are the criteria, and the answer shows why the decision was made, with a probability for each option, so your code can decide what happens next.