Tutoring AI guide
Token compression for AI tutoring
AI tutors need to reference lesson material, but most of the lesson is irrelevant to a specific student question. SuperCompress keeps the relevant explanatory sections and drops everything else.
The tutoring context problem
A student asks: "Why does the quadratic formula work?" The AI tutor loads the full algebra curriculum chapter (3,000+ tokens). Only the ~200 tokens explaining the quadratic formula derivation are relevant. The rest — chapters on linear equations, factoring, and graphing — wastes tokens.
Integration
from supercompress import Compressor
comp = Compressor()
def tutor(student_question, lesson_text):
result = comp.compress(lesson_text, student_question)
return llm.generate(
f"Lesson context:\n{result.compressed_text}\n\nStudent: {student_question}"
)Frequently asked questions
Does compression work with adaptive learning paths?
Yes. The student's learning history compresses well against each new question.
Can I use this with existing tutoring platforms?
Yes. Add SuperCompress as middleware in your tutoring AI backend.
Try it yourself
Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.