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 |
Da utilizzare quando si affrontano compiti di ragionamento complessi che richiedono logica passo-passo, aritmetica multi-passo, ragionamento basato sul buon senso, manipolazione simbolica o problemi in cui il semplice suggerimento fallisce - fornisce una guida completa alla catena di pensiero e alle relative tecniche di suggerimento (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) con modelli, matrici decisionali e modelli supportati dalla ricerca Fonte: neolabhq/context-engineering-kit.