Library · Science
Chain of Draft (CoD) Prompting Technique
Chain of Draft (CoD) Prompting Technique Source: "Chain of Draft: Thinking Faster by Writing Less" — arXiv 2502.18600 (Feb 2025) ------------------------------------------------------------------
Prompt text
How it works
Conceptual workflow
Derived from this prompt's instructions: adopt doing, then return a single reply. This is a map of the text, not a live model execution.
vcp · prompts/chain-of-draft
run@once
- receive
- role
- explain
- output
Stage 1 / 4 · receive
Receive the user turn
The user sends a task, command, or line of dialogue. That text is the only new input for this turn.
Artifact · user-turn.txt
User input
Review this artifact.
Rule in force
This turn’s input is the only new information.
Visible reply
(waiting — role not adopted yet)
Illustration · not a live model run
Prompt evidence
Chain of Draft (CoD) Prompting Technique
Source: "Chain of Draft: Thinking Faster by Writing Less" — arXiv 2502.18600 (Feb 2025)
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TECHNIQUE OVERVIEW:
Chain of Draft constrains each reasoning step to ≤5 words, forcing minimal but essential
intermediate thinking. On GSM8k math: 91% accuracy vs CoT's 95%, using only 7.6% of tokens.
Latency reduction up to 76%.
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BASIC SYSTEM PROMPT:
Think step by step, but only keep a minimum draft for each thinking step, with 5 words at most.
Return the answer after a #### separator.
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EXTENDED SYSTEM PROMPT (for complex tasks):
Think step by step. For each reasoning step, write a minimal draft of 5 words or fewer — only
the essential operation or transformation. Do not explain; just note what you are doing.
After all steps, write #### on its own line, then give the final answer.
Example format:
Step 1: [≤5 words]
Step 2: [≤5 words]
Step 3: [≤5 words]
####
[Final answer here]
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USAGE NOTES:
- Best for: math word problems, logical reasoning, multi-step calculations
- Not ideal for: creative writing, open-ended generation, tasks requiring explanation
- Works with: GPT-4, Claude 3+, Gemini 1.5+
- Token savings: ~92% vs standard CoT; latency: up to 76% lower
- Accuracy tradeoff: ~4% below CoT on math benchmarks — acceptable for most real-world use
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FEW-SHOT EXAMPLE:
User: "Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 balls.
How many tennis balls does he have now?"
Model:
Step 1: 2 cans × 3 balls
Step 2: 6 new balls
Step 3: 5 + 6 = 11
####
11Template
A system prompt still belongs in the library
Engineering
Compile, test, constrain, or search
Conceptual workflow · 4.5s / stage · 1/4
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