Chain-of-Thought Prompting — Claude
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
The prompt
Before answering, list the 3 possible root causes of this bug, evaluate each against the stack trace below, then state which is most likely and why. [paste stack trace]
What to replace
Swap these placeholders for your own details before running the prompt:
[paste stack trace]your own value
Pro tip: Chain-of-thought helps most on math, logic, debugging, and multi-criteria decisions — skip it for simple factual lookups, it just adds noise.
How to use this prompt
- Copy the prompt using the button above.
- Replace [paste stack trace] with your own details — the more specific you are, the better the output.
- Paste it into Claude and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Learn the technique
Chain-of-Thought Prompting
Module 2 — Core Techniques · Prompt Engineering Fundamentals
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Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
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