Introduction
AI is advancing rapidly, solving increasingly complex problems. However, a new technique called Chain of Draft (CoD) is making AI both faster and more efficient by optimizing how it processes information.
In this blog, we’ll explore what Chain of Draft is, how it works, and why AI engineers should consider using it. For more insights into AI trends, visit our AI blog.
What is Chain of Thought (CoT)?
Before we dive into Chain of Draft, let’s first understand Chain of Thought (CoT). CoT is a method that enables AI to break down problems into step-by-step solutions, similar to human reasoning.
For example, consider this math problem:
“Jason had 20 lollipops. He gave some to Denny and now has 12 left. How many did he give to Denny?”
A basic AI response might simply be:
Answer: 8.
However, an AI using Chain of Thought would break it down like this:
- Jason had 20 lollipops.
- He gave some away and now has 12.
- To find out how many he gave away: 20 – 12 = 8.
- Answer: 8.
While CoT improves transparency, it also requires more processing time and resources.
What is Chain of Draft (CoD)?
Chain of Draft is a faster and more efficient alternative to Chain of Thought. Instead of generating extensive reasoning, it focuses on the key steps required to reach a solution.
Using Chain of Draft, the AI would respond to the same question like this:
20 – x = 12 → x = 20 – 12 → x = 8.
This approach maintains accuracy while reducing processing time and token usage, making AI responses:
- Faster
- More cost-effective
- More efficient
Performance Comparison: CoT vs. CoD
To understand the benefits of Chain of Draft, let’s examine real-world data comparing CoT and CoD across different AI models and tasks.
1. Date Understanding Evaluation
| Model | Prompt | Accuracy | Token Count | Latency |
|---|---|---|---|---|
| GPT-4o | Standard | 72.6% | 5.2 | 0.6 s |
| CoT | 90.2% | 75.7 | 1.7 s | |
| CoD | 88.1% | 30.2 | 1.3 s | |
| Claude 3.5 Sonnet | Standard | 84.3% | 5.2 | 1.0 s |
| CoT | 87.0% | 172.5 | 3.2 s | |
| CoD | 89.7% | 31.3 | 1.4 s |
2. Sports Understanding Evaluation
| Model | Prompt | Accuracy | Token Count | Latency |
|---|---|---|---|---|
| GPT-4o | Standard | 90.0% | 1.0 | 0.4 s |
| CoT | 95.9% | 28.7 | 0.9 s | |
| CoD | 98.3% | 15.0 | 0.7 s | |
| Claude 3.5 Sonnet | Standard | 90.6% | 1.0 | 0.9 s |
| CoT | 93.2% | 189.4 | 3.6 s | |
| CoD | 97.3% | 14.3 | 1.0 s |
3. Coin Flip Evaluation
| Model | Prompt | Accuracy | Token Count | Latency |
|---|---|---|---|---|
| GPT-4o | Standard | 73.2% | 1.0 | 0.4 s |
| CoT | 100.0% | 52.4 | 1.4 s | |
| CoD | 100.0% | 16.8 | 0.8 s | |
| Claude 3.5 Sonnet | Standard | 85.2% | 1.0 | 1.2 s |
| CoT | 100.0% | 135.3 | 3.1 s | |
| CoD | 100.0% | 18.9 | 1.6 s |
Key Insights
- Accuracy: CoD maintains or even surpasses CoT in certain tasks.
- Token Efficiency: CoD significantly reduces the number of tokens used, leading to cost savings.
- Lower Latency: Shorter responses improve speed, making AI interactions more responsive.
Why AI Engineers Should Adopt Chain of Draft
If you are developing AI models, here’s why you should consider switching to Chain of Draft:
- Lower Computational Costs – Reduces token usage, saving money on processing power.
- Faster Processing Times – Ideal for real-time applications like chatbots and automation.
- Maintains Accuracy – Streamlined reasoning without compromising correctness.
- Easy to Implement – Requires only a simple change in prompt structure.
To see how OpenAI’s latest advancements compare, check out GPT-4.5’s debut.
Final Thoughts
Chain of Draft is a major step forward in AI efficiency. By eliminating unnecessary verbosity, AI models can think faster, cost less to run, and perform better in real-world applications.
If you’re working with AI, now is the time to try Chain of Draft and see how much more efficient your models can become!
For further reading, check out the original research paper from Zoom Communications: Chain of Draft: Thinking Faster by Writing Less.
