The Chain-of-Thought Prompt Builder wraps any question in the scaffolding that makes language models reason carefully instead of blurting a first guess. Chain-of-thought (CoT) prompting — asking the model to “think step by step” before answering — is one of the most reliable, well-studied techniques for improving accuracy on maths, logic, and multi-step analysis. This tool assembles a clean CoT prompt for you in the browser, tuned to the domain and difficulty you choose.
How it works
You provide three things: the question or task, the domain (maths, coding, analysis, planning, or general), and the complexity level. The builder then constructs a prompt with explicit reasoning instructions appropriate to that combination. For higher complexity it adds stronger scaffolding — decompose the problem, work each sub-step in order, check intermediate results, and only then state a final answer clearly labelled and separated from the reasoning. For domain-specific tasks it adds the right framing, such as “show each calculation” for maths or “state your assumptions” for analysis.
The output places the reasoning instruction after the question, mirroring the classic “Let’s think step by step” pattern, and structures the request so the final answer is easy to extract. Optionally it includes a placeholder for a worked example, since a single few-shot demonstration often lifts CoT accuracy further.
Tips and notes
CoT pays off on multi-step problems and adds little on simple lookups, so reserve it for tasks where the model genuinely has to reason. During development, keep the reasoning visible — it is the fastest way to find where the model goes wrong. In production you can ask it to reason silently and return only the labelled final answer, or parse the answer out of the response. If accuracy still lags, add one or two worked examples (few-shot CoT), which tends to beat zero-shot CoT on the hardest tasks.