Chain-of-Thought Prompting Technique

A Chain-of-Thought Prompting Technique is a prompt engineering reasoning elicitation technique that instructs large language models to explicitly generate intermediate reasoning steps before producing final answers.

• Context:
  • It can typically elicit Step-by-Step Reasoning through explicit instructions.
  • It can typically improve Complex Question Answering via reasoning chain generation.
  • It can typically enhance Problem-Solving Accuracy through deliberative processes.
  • It can typically make Model Reasoning more transparent and interpretable.
  • It can typically support Multi-Step Instruction Following with intermediate checkpoints.
  • ...
  • It can often use Simple Prompts like "Let's think step by step".
  • It can often include Reasoning Examples in few-shot configurations.
  • It can often combine with Test-Time Compute for extended reasoning.
  • It can often generate Multiple Reasoning Paths for consistency checking.
  • ...
  • It can range from being a Zero-Shot Chain-of-Thought Prompting Technique to being a Few-Shot Chain-of-Thought Prompting Technique, depending on its chain-of-thought prompting example provision.
  • It can range from being a Simple Chain-of-Thought Prompting Technique to being a Self-Consistency Chain-of-Thought Prompting Technique, depending on its chain-of-thought prompting aggregation method.
  • ...
  • It can enable IMO Problem Solutions achieving 35/42 scores.
  • It can facilitate Mathematical Reasoning in LLM applications.
  • It can support Logical Deduction Tasks through explicit steps.
  • It can improve Arithmetic Calculations via intermediate results.
  • It can enhance Commonsense Reasoning with thought verbalization.
  • ...
• Example(s):
  • Zero-Shot CoT Implementations, such as:
    • Step-by-Step Prompt, using single instructive phrase.
    • Think-Aloud Prompt, encouraging reasoning verbalization.
  • Few-Shot CoT Implementations, such as:
    • Example-Based CoT, including solved problems in prompt.
    • Demonstration CoT, showing reasoning patterns to follow.
  • Self-Consistency CoT Implementations, such as:
    • Multi-Path CoT, generating multiple chains for voting.
    • Ensemble CoT, aggregating diverse reasoning paths.
  • ...
• Counter-Example(s):
  • Direct Prompting, asking for answers without reasoning steps.
  • Hidden Chain-of-Thought, where internal reasoning is not user-visible.
  • Single-Step Prompting, lacking intermediate step generation.
• See: Prompt Engineering, LLM Reasoning Technique, Few-Shot Learning, Test-Time Compute Technique.

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