thought-based-reasoning

Chain-of-Thought (CoT) prompting and its variants encourage LLMs to generate intermediate reasoning steps before arriving at a final answer, significantly improving performance on complex reasoning tasks. These techniques transform how models approach problems by making implicit reasoning explicit.

| Technique | When to Use | Complexity | Accuracy Gain |

|-----------|-------------|------------|---------------|

| Zero-shot CoT | Quick reasoning, no examples available | Low | +20-60% |

| Few-shot CoT | Have good examples, consistent format needed | Medium | +30-70% |

| Self-Consistency | High-stakes decisions, need confidence | Medium | +10-20% over CoT |

| Tree of Thoughts | Complex problems requiring exploration | High | +50-70% on hard tasks |

| Least-to-Most | Multi-step problems with subproblems | Medium | +30-80% |

| ReAct | Tasks requiring external information | Medium | +15-35% |

| PAL | Mathematical/computational problems | Medium | +10-15% |

| Reflexion | Iterative improvement, learning from errors | High | +10-20% |

Use CoT techniques when:

• Multi-step arithmetic or math word problems
• Commonsense reasoning requiring logical deduction
• Symbolic reasoning tasks
• Complex problems where simple prompting fails

Start with:

• Zero-shot CoT for quick prototyping ("Let's think step by step")
• Few-shot CoT when you have good examples
• Self-Consistency for high-stakes decisions

L2 Content (loaded when core techniques needed):

• See: [references/core-techniques.md](./references/core-techniques.md)

- Chain-of-Thought (CoT) Prompting

- Zero-shot Chain-of-Thought

- Self-Consistency Decoding

- Tree of Thoughts (ToT)

- Least-to-Most Prompting

- ReAct (Reasoning + Acting)

- PAL (Program-Aided Language Models)

- Reflexion

L3 Content (loaded when decision guidance and best practices needed):

• See: [references/guidance.md](./references/guidance.md)

- Decision Matrix: Which Technique to Use

- Best Practices

- Common Mistakes

- References