Skip to main content
Whether you’re testing, refining, or scaling, evaluators help you do it better and faster. Here are four ways you can use them effectively.

​
Optimize your product

Evaluators help you analyze specific characteristics of large language models (LLMs)–generated text. For example, if you’re developing a feature that evaluates a student’s vocabulary, you might increase vocabulary difficulty while keeping sentence structure simpler.
Three control dials showing vocabulary set higher than sentence structure.
You can set targets for how closely sentence structure and vocabulary should match the grade-level appropriateness of generated text. Then use the Sentence Structure Evaluator and Vocabulary Evaluator on sample outputs from your LLM to confirm they fall within acceptable ranges for both dimensions. Or, if you’re creating a feature to support students reading aloud—focusing on rhythm and flow rather than complex words—you might optimize for different text complexity parameters.
Three control dials showing sentence structure and conventional language set higher than vocabulary.
In that case, you would look for lower vocabulary complexity when evaluating your text samples.

​
Monitor consistency

You don’t want your AI model to produce unpredictable results. Models can drift, or small system changes can alter behavior subtly over time. Without safeguards, unexpected content can appear before anyone notices. Set up regular regression tests to measure the consistency of LLM-generated text over time. These tests help you confirm that results stay stable from week to week or day to day, ensuring your user experience remains reliable.

​
Select the right model

With new models emerging constantly, you need a consistent method to compare them. By establishing a gold set of expected scores across key parameters—such as grade level, topic, or text type—you can use evaluators as a standardized measurement tool. They help you weigh speed, quality, and cost tradeoffs, so you can choose or update models confidently while automatically detecting drift from your baseline.

​
Build trust with your users

Districts and educators increasingly want transparency about how edtech products ensure quality. Evaluators let you demonstrate that your AI-generated content aligns with research-backed learning principles. Explaining your evaluation approach helps educators understand the rigor behind your system and builds confidence that your product delivers consistent, high-quality results.