# Chain-of-Thought Prompting Cheat Sheet
_Getting reasoning out of the model and keeping it out of the answer_
Chain-of-thought trades tokens for accuracy on multi-step problems. It helps on arithmetic, logic and planning — and costs you on lookups.
> Difficulty: intermediate  
> Version: 1.0  
> Updated: 2026-03-29  
> Categories: Prompt Engineering  
> Tags: Chain Of Thought, Claude, Openai

Source: https://invitationbuddy.com/cheat-sheet/chain-of-thought-cheat-sheet

---

## Variants
| Technique | How | Best for |
| --- | --- | --- |
| Zero-shot CoT | Append "Think step by step."  A cheap first try on any reasoning task |
| Few-shot CoT | Show worked examples including the reasoning  Domain problems with a house method |
| Structured CoT | Ask for reasoning inside tags, answer outside  Anything whose output is parsed |
| Self-consistency | Sample n times, take the majority answer  High-stakes arithmetic; n× the cost |
| Least-to-most | Ask it to decompose, then solve each part  Problems that nest |

## Where it does NOT help
| Task | Why CoT hurts |
| --- | --- |
| Factual lookup | Reasoning about a fact you either know or do not adds a chance to talk yourself out of it |
| Classification into few labels | The extra tokens mostly rationalise the first instinct |
| Creative writing | Planning aloud flattens voice |
| Latency-critical paths | You are paying real milliseconds for the tokens |

## FAQs
**Does chain-of-thought make answers more accurate?**
On multi-step reasoning — arithmetic, logic, planning — usually yes. On factual recall and simple classification it often makes them worse, because it gives the model room to talk itself out of a correct first instinct.

**How do I keep the reasoning out of what I show the user?**
Ask for it inside tags and parse them off, or use a model with a native reasoning channel. Never ask it to "think but do not show your thinking" — that just moves the reasoning into the answer.

---
_Generated from https://invitationbuddy.com/cheat-sheet/chain-of-thought-cheat-sheet_