Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where…
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%
don't have the plugin yet? install it then click "run inline in claude" again.