customaize-agent: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 |

在处理需要逐步逻辑、多步算术、常识推理、符号操作的复杂推理任务或简单提示失败的问题时使用 - 通过模板、决策矩阵和研究支持的模式提供思想链和相关提示技术（零样本 CoT、自我一致性、思想树、最少到最多、ReAct、PAL、反射）的全面指南 来源：neolabhq/context-engineering-kit。