Chain-of-Thought Prompting: A Practical Guide

Bottom line: Reasoning improves when you ask the model to show its work. Chain-of-thought prompting is the simplest way to get more accurate answers on complex tasks.

Why reasoning breaks down

Language models predict the next token. On simple questions, the answer is immediate. On hard questions, the model may jump to a conclusion and miss an intermediate step. Asking for step-by-step reasoning forces the model to work through the problem.

Zero-shot chain of thought

The simplest version is adding "Think step by step" to the end of your prompt. It works surprisingly well on word problems, logic puzzles, and math.

A train leaves station A traveling at 60 mph. Another train leaves station B traveling at 40 mph. The stations are 200 miles apart. When do they meet?

Think step by step.

Few-shot chain of thought

For harder tasks, show the model one or more examples of the reasoning format you want. The examples teach the model both the style and the depth of explanation.

Q: Roger has 5 balls. He buys 3 more cans, each with 2 balls. How many balls does he have now?
A: Roger starts with 5 balls. He buys 3 cans with 2 balls each, so 3 times 2 is 6 balls. 5 plus 6 is 11. The answer is 11.

Q: ...

Self-consistency

Even with chain of thought, a single sample can go wrong. Self-consistency generates multiple answers and takes a majority vote. It trades cost and latency for higher accuracy.

When to use it

• Math and arithmetic problems.
• Multi-step logical reasoning.
• Decision making with tradeoffs.
• Debugging and root cause analysis.
• Any task where the final answer depends on intermediate conclusions.

When to skip it

Do not use chain of thought for tasks where speed matters more than accuracy, or where the answer is a simple lookup. Also avoid exposing internal reasoning to end users if it contains sensitive logic.

Tips for better chains

• Ask the model to explain before answering.
• Provide examples that match the desired reasoning style.
• Use structured output to separate reasoning from the final answer.
• For sensitive domains, keep reasoning internal and show only the conclusion.

Published 2026-06-12