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.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.