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Using Classical Chinese for Prompt Compression: A Creative Approach

Using Classical Chinese for Prompt Compression: A Creative Approach

@vikingmute

今天这个 Caveman 又火了：github.com/JuliusBrussee/…

原理还是一样，用更加凝练的语言让大模型输出内容，去掉废话，说是能减少 75%。

我又想到：怎么还没有人做一个中文类似的啊，用文言文可以说是非常简单了，这可以是获取 star 的最简单方式了。

“您好！关于您遇到的 React 组件重复渲染问题，原因很可能是在每次渲染时都创建了新的对象引用。当您将内联对象作为 prop 传递时，React 的浅比较会认为它是不同的对象，从而触发重新渲染。我建议您使用 useMemo 来对该对象进行记忆化处理。”

=>

“重绘者，新对象之故也。内联为 prop，浅比异。用 useMemo 记之。” 这都力省多少 token 了啊 Show More

Apr 6, 2026, 8:09 AM View on X

3 Replies

1 Retweets

5 Likes

1,802 Views  Viking @vikingmute

One Sentence Summary

The author proposes using the high information density of Classical Chinese as a novel approach for LLM prompt compression, demonstrating its effectiveness with a React-related example.

Summary

Inspired by the Caveman project, which reduces LLM token consumption by streamlining language, the author proposes an intriguing idea: leveraging the extremely high information density of Classical Chinese as an efficient means of prompt compression in a Chinese context. Through a comparative example involving a React component rendering issue, the author demonstrates the potential of Classical Chinese to significantly reduce token count while maintaining semantic integrity, offering a highly creative optimization direction for Prompt Engineering.

AI Score

80

Influence Score 6

Published At Today

Language

Chinese

Tags

Prompt Engineering

LLM

Token Optimization

Classical Chinese

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Using Classical Chinese for Prompt Compression: A Creativ...