Analysis updated 2026-07-13 · repo last pushed 2025-03-11
Build a math tutoring app that gives faster, cheaper answers by reducing AI reasoning text.
Create a sports statistics Q&A tool that cuts API costs by generating only essential reasoning notes.
Run the included evaluations to compare standard AI reasoning against the lean Chain of Draft approach.
Integrate the technique into any app using OpenAI or Claude models to speed up response times.
| deftruth/chain-of-draft | 0xhassaan/nn-from-scratch | 0xzgbot/hermes-comfyui-skills | |
|---|---|---|---|
| Stars | — | 0 | 0 |
| Language | — | Python | — |
| Last pushed | 2025-03-11 | — | — |
| Maintenance | Stale | — | — |
| Setup difficulty | easy | moderate | easy |
| Complexity | 2/5 | 4/5 | 1/5 |
| Audience | developer | developer | designer |
Figures from each repo's GitHub metadata at analysis time.
Requires an API key for OpenAI or Claude to run the evaluations.
Chain of Draft helps AI language models solve problems faster and cheaper by teaching them to think more like humans do. When today's AI tackles a reasoning task, it typically writes out long, verbose step-by-step explanations. This project instead prompts the AI to jot down only the essential information needed at each step, much like a person scribbling quick notes in the margin. The result is that the AI reaches the right answer just as accurately, but uses as little as 7.6% of the text it would normally generate. The core idea is about reducing waste in the AI's intermediate reasoning. Standard step-by-step prompting has the model write out full sentences and explanations for every part of its thought process. Chain of Draft swaps in a different set of instructions and examples that encourage the model to be minimalistic. By generating only short, critical insights instead of full paragraphs, the overall process speeds up and costs less, since AI usage is typically billed by the volume of text generated. This project would be useful for anyone building applications that rely on AI reasoning, especially when cost and response time matter. For example, a startup building a math tutoring app or a tool that answers complex sports statistics questions could use this approach to get answers to their users faster and at a fraction of the API cost. The repository includes ready-to-run evaluations across a few task types, including math word problems, date understanding, sports trivia, and coin-flip reasoning. The repository supports popular models from OpenAI and Claude, as well as any model that uses an OpenAI-compatible interface. Users can run the provided evaluation script to test the approach themselves, with all the prompts and examples stored in simple configuration files. The tradeoff the project makes is straightforward: it bets that brevity won't hurt accuracy, and the results suggest that cutting the fat from AI reasoning actually preserves performance while saving significant time and money.
Chain of Draft is a prompting technique that makes AI language models solve problems with far less text by writing only essential notes instead of long explanations, cutting costs and response time while keeping accuracy.
Stale — no commits in 1-2 years (last push 2025-03-11).
The license is not specified in the repository explanation, so permission terms are unknown.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.