What is Chain-of-Thought Prompting?
Chain-of-Thought (CoT) Prompting is a prompting technique where an AI model is instructed to explain its thinking process step by step before giving a final answer. This significantly improves quality on complex reasoning tasks.
The technique was introduced by Google researchers in 2022 and has since become one of the most important prompting techniques.
Why Does Chain-of-Thought Work?
LLMs generate text token by token. With complex problems, direct answering often leads to errors because the model doesn't have enough "room to think." CoT forces the model to formulate intermediate steps, which reduces the error rate on mathematical and logical tasks by up to 50%.
How Much Does Chain-of-Thought Really Help?
Research shows impressive improvements from Chain-of-Thought prompting, especially on math and logic tasks. Here are the concrete benchmark results from the key papers:
CoT Performance Comparison
Accuracy of different prompting methods on popular benchmarks (PaLM-540B)
Source: Wei et al. 2022, Wang et al. 2022
Key Research Insights
GSM8K: +218% Improvement
For math word problems, Chain-of-Thought increased accuracy from 17.9% to 56.9% – an improvement of over 200%.
Wei et al. 2022Self-Consistency: +17.5 Percentage Points
By majority voting over 40 reasoning paths, Self-Consistency improved GSM8K accuracy from 56.9% to 74.4%.
Wang et al. 2022Symbolic Reasoning: +794% on Last Letter
For symbolic tasks like letter concatenation, accuracy jumped from 6.6% to 59% – nearly 8x improvement.
Wei et al. 2022GSM8K
MathSVAMP
MathStrategyQA
ReasoningCommonsenseQA
CommonsenseLast Letter
SymbolicCoin Flip
SymbolicCoT Variants: From Zero-Shot to Graph of Thoughts
Since Chain-of-Thought Prompting was introduced in 2022, numerous variants and advancements have been researched. From the simple Zero-Shot variant to complex tree structures like Tree of Thoughts – each technique has its strengths and optimal use cases.
The following overview compares all major CoT variants:
Chain-of-Thought Variants Comparison
All scientifically founded CoT techniques from current research papers
When to Use Chain-of-Thought?
- Mathematical Problems: Word problems, calculations, statistics
- Logical Reasoning: "If A, then B" chains
- Multi-Step Tasks: Problems requiring multiple steps
- Code Debugging: Systematic error analysis
- Decision Making: Pro/con evaluations
Practical Examples
Without Chain-of-Thought
Question: "A train travels 120 km in 2 hours. How long does it take for 300 km?"
Answer: "5 hours" (often wrong or without justification)
With Chain-of-Thought
Question: "A train travels 120 km in 2 hours. How long does it take for 300 km? Think step by step."
Answer: "Step 1: Calculate speed: 120 km ÷ 2 h = 60 km/h. Step 2: Time for 300 km: 300 km ÷ 60 km/h = 5 hours."
Tips for Effective CoT
- Be explicit: "Show every step of your reasoning"
- Request structure: "Number your steps"
- Demand justifications: "Explain why you take each step"
- Combine with self-verification: "Verify your result at the end"
Limitations of Chain-of-Thought
- More Tokens: CoT answers are longer and cost more
- Not Always Necessary: For simple questions, CoT is overkill
- Can Mislead: Convincing-sounding but incorrect reasoning chains
- Model-Dependent: Smaller models benefit less from CoT
Conclusion
Chain-of-Thought Prompting is one of the most effective techniques for leveraging the reasoning capabilities of LLMs. For complex tasks, "Think step by step" should be part of the standard repertoire. The technique is easy to apply and delivers measurably better results.