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

When to Use Thought-Based Reasoning

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

Progressive Loading

L2 Content (loaded when core techniques needed):

• See: 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
  • Decision Matrix: Which Technique to Use
  • Best Practices
  • Common Mistakes
  • References