Evaluators

Evaluators help you to measure the attributes of LLM-generated text through the lens of learning science:

• Optimize features: Sharpen AI-generated content so it aligns with pedagogy and your goals.
• Maintain performance: Use evaluators as product analytics to confirm LLM output stays on target.
• Select models: Compare candidate models by testing their output against the evaluators.

Repository contents

Folder	Contents
evals	Evaluators (with prompts, input/output schemas, and Python notebooks), grouped by family: Text Complexity, Feedback, Academic Standards Alignment, and Durable Skills
sdks	Python and TypeScript
demos	Sample apps that demonstrate SDK use
datasets	Expert-annotated datasets used to create and validate evaluators
scripts	Repo check harness — run python3 scripts/check.py --fix before committing (see scripts/README)
LICENSE	Open source license details

Quickstart

Check out the Quickstart to start using Evaluators.

This is the beginning of the README. Read the rest at the original. Read the full README ↗