Chain of Thought (CoT)

Details
Full Name

Chain of Thought Prompting

Core Concepts:

Step-by-Step Reasoning

Explicitly show intermediate reasoning steps before reaching a conclusion

Reasoning Transparency

Make the thought process visible, not just the final answer

Intermediate Representations

Break complex problems into smaller, manageable steps

Error Reduction

Exposing reasoning allows detection of logical errors mid-process

Complex Task Decomposition

Handle multi-step problems that cannot be solved in one jump

Zero-Shot CoT

Simple prompt like "Let’s think step by step" to trigger CoT behavior

Few-Shot CoT

Provide examples with reasoning chains to guide the model

Self-Consistency

Generate multiple reasoning paths and select most consistent answer

Key Proponents

Wei et al. (Google Research, 2022), "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models"

Historical Context

Breakthrough in LLM prompting research, significantly improved performance on reasoning tasks (math, logic, common sense)

When to Use:

  • Complex reasoning problems (multi-step math, logic puzzles)

  • When you need to verify the reasoning process, not just the answer

  • Debugging incorrect LLM outputs by seeing where reasoning went wrong

  • Teaching or explaining complex topics where steps matter

  • Problems requiring planning or strategy

  • Any task where intermediate steps provide value

  • Tree of Thoughts (ToT): Extension allowing branching and backtracking

  • Self-Consistency: Sample multiple reasoning paths

  • Least-to-Most Prompting: Build up from simple to complex

Example Prompt Pattern:

Problem: [Complex question]
Let's solve this step by step:
1. [First step]
2. [Second step]
...
Therefore: [Conclusion]

Current Status:

  • The core finding (Wei et al., NeurIPS 2022) is stable, but its practical relevance is cutoff-bound: reasoning models (late 2024 onward) internalise chain-of-thought during training — OpenAI’s own docs now advise against "think step by step" prompts for those models

  • Treat CoT output as a useful but unreliable window into model reasoning: Turpin et al., "Language Models Don’t Always Say What They Think" (2023) and Anthropic’s 2025 follow-up show stated reasoning can be unfaithful to the actual decision factors — not a safety or audit guarantee