Mind Blowing New Prompt Engineering Method
AI Summary
Summary of ‘Chain of Drafts’ Prompting Technique
- Introduction to Chain of Drafts
- New prompting method introduced as an improvement over the traditional Chain of Thought prompting.
- Aims to make prompt engineering more efficient and concise.
- Difference from Existing Techniques
- Simple Prompt: Direct request for output from LLM.
- Chain of Thought: Step-by-step reasoning; however, it tends to be verbose and costly in token usage.
- Chain of Drafts: Encourages concise reasoning, limiting each reasoning step to a maximum of five words.
- Research Findings
- Various experiments comparing:
- Standard Prompt
- Chain of Thought
- Chain of Drafts
- Results showed:
- For GPT-4:
- Standard Prompt: 53% accuracy, high token usage.
- Chain of Thought: 95% accuracy, high latency and token usage.
- Chain of Drafts: 91% accuracy, significantly reduced tokens and improved latency.
- Common Sense Reasoning Tests: Chain of Drafts yielded similar or superior results while maintaining lower token usage and latency.
- Advantages of Chain of Drafts
- Reduced verbosity leads to lower token costs and faster processing.
- Mimics human cognitive processes in problem-solving by prioritizing outlines and essential steps.
- Useful for automations with multiple components optimizing for speed and cost.
- Limitations
- Less effective with smaller models.
- Inconsistencies noted without providing short examples for context.
- Conclusion
- Chain of Drafts is a promising technique for enhancing prompt engineering efficiency.
- Encourages experimentation for those interested in leveraging AI for automation and problem-solving.
Link to the research paper is provided in the video description.