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Ensure that your AI-generated text passage properly caters to its intended grade level before shipping.

What you’ll do

  • Score a passage with the Grade Level Appropriateness evaluator
    • Treat a grade band above your target as a failed check
    • When it fails, use the Vocabulary Complexity evaluator for the words and reasoning to send back to your model
  • Ask your model to rewrite the passage, if needed
  • Score the rewritten passage before it reaches a student

What you’ll need

  • Node.js or Python
  • Google API key ↗ (needed for Grade Level Appropriateness and Vocabulary Complexity)
  • OpenAI API key ↗ (needed for Vocabulary Complexity)
  • Text passage and the grade band you want it to stay within

Steps

1

Install the SDK

Install the package for the language your pipeline uses:
TypeScript
2

Assess the grade level for your text passage

Inputs must be de-identified. Do not submit students' Personally Identifiable Information (PII) or any regulated or sensitive personal information.
Grade Level Appropriateness takes a text passage and returns the grade band at which a student can read independently:
3

Determine whether the result meets your target

Compare the returned grade band with your desired target. If the result is higher, it means the passage is too difficult for your audience and should not ship.
4

Rewrite the passage, if needed

If the result grade band is too high, run Vocabulary Complexity on the same passage.Share the returned tier_2_words, tier_3_words, and reasoning with your model to help it rewrite the message for the target grade level.
Keep track of how many times your passage must be rewritten. A growing count is a sign the base prompt needs a change.Check out Improve quality by refining prompts for more details on this use case.
5

Score the rewrite before it ships

Rerun Grade Level Appropriateness on your rewritten passage. Ship only if the result is at or below your target grade level.

Resource Library - Vet content quality in production

Score every output in your pipeline, then rewrite and re-check anything that misses the rubric.

Quickstart

Run an evaluator in the Evaluators playground, a Python notebook, or with the SDK.

Tutorial: Monitor quality in production

Track grade band across a sample of outputs and catch a shift over time.

Tutorial: Improve quality by refining prompts

Compare two prompt versions on grade band, vocabulary, and sentence structure.