Education guide
Token compression for education AI
Education AI applications process curriculum content, student responses, and assessment data. These are long-form contexts where 60-80% of tokens are irrelevant to the specific educational query.
Education AI token spend
A tutoring AI sends: the curriculum context (1,000-5,000 tokens), student's previous answers (500-2,000 tokens), current question (50-200 tokens), and assessment criteria (200-500 tokens). Total: 1,750-7,700 tokens per tutoring interaction.
Curriculum compression
from supercompress import Compressor
comp = Compressor()
def tutor_response(curriculum_section, student_question, student_history):
context = f"Curriculum: {curriculum_section}\nHistory: {student_history}"
result = comp.compress(context, student_question)
return llm.generate(
f"Tutor the student based on:\n{result.compressed_text}"
)Frequently asked questions
Does compression affect educational accuracy?
No. SuperCompress preserves lines containing educational content relevant to the question. Irrelevant curriculum sections are removed.
Can I use it with Khan Academy or Coursera content?
Yes. Compress any educational text before sending it to an LLM for processing.
Try it yourself
Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.