thought-based-reasoning

SKILL.md

Thought-Based Reasoning Techniques for LLMs

Overview

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.

Quick Reference

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%