How to use chain-of-thought prompting for complex reasoning

Chain-of-thought prompting encourages the model to write out its intermediate reasoning steps before giving a final answer. This dramatically improves accuracy on tasks that require arithmetic, logical deduction, or multi-step planning — tasks where jumping straight to an answer leads to errors.

The insight is that transformers process tokens sequentially. When forced to write reasoning steps, each step is visible to the model when it generates the next one, which allows it to self-correct and carry forward intermediate results accurately.

The easiest way to trigger CoT is to append 'Think step by step' or 'Let's think through this carefully' to your prompt. This alone improves performance on arithmetic and logic tasks by 20–50% in most evaluations.

For even better results on specific problem types, use a more targeted instruction: 'First, identify all the relevant information. Then, determine what the question is asking. Finally, work through the calculation step by step and double-check your arithmetic.'

Providing examples of good reasoning chains (few-shot CoT) further improves performance. Structure each example as: Question → Reasoning (step by step) → Answer. Use 2–4 examples that cover different cases you expect in production.

The examples teach the model the reasoning format you want — not just that it should reason, but how. This is particularly valuable for domain-specific tasks where the reasoning structure is non-obvious.

CoT significantly helps: multi-step arithmetic, logic puzzles, planning tasks, debugging complex code, legal or medical reasoning chains, and any task where the model must track multiple variables or constraints.

CoT does not help and may hurt: simple classification, factual recall, or sentiment analysis. For these tasks, asking the model to reason first can introduce unnecessary hedging and slow down responses. Use CoT selectively.

In production, you usually want the reasoning to happen silently and only the final answer to be returned. Use a two-call approach: first call with 'Think step by step' and extract the reasoning; second call with 'Given this reasoning: [reasoning], what is the final answer?' to extract a clean response.

Alternatively, use structured outputs: ask the model to return `{"reasoning": "...", "answer": "..."}` as JSON, then display only the answer in your UI while logging the reasoning for debugging.