Chinese chain-of-thought (CoT) prompt builder: task, conditions, step-by-step reasoning, output format. In your browser.
Chinese Chain-of-Thought (CoT) Prompt Builder
Assemble a clean, structured Chinese-language chain-of-thought (CoT) prompt from a simple form — task, context, reasoning-step requirements, output format and constraints — that makes the model reason step by step before it answers, then copy it straight into DeepSeek, Qwen (通义千问), Doubao or Kimi. Everything is built in your browser; nothing is sent to a server and no model is called.
Tip: this builder only assembles text. Copy the result into DeepSeek / Qwen / Doubao / Kimi yourself — no model is called and nothing is sent anywhere. Native reasoning models (DeepSeek-R1 etc.) already do CoT, so you can skip the "reason step by step" line for them.
How the Chinese chain-of-thought prompt builder works
State the task / question clearly
In the first box, give the concrete task or question you want the model to solve — e.g. "check whether the answer to this word problem is correct" or "break this requirement down and propose an implementation". This line is the heart of the chain-of-thought prompt; the more specific the task, the more on-point the step-by-step reasoning.
Supply context and known conditions
Fill in the relevant background, known data, definitions or assumptions. Chain-of-thought reasoning runs on facts: hand the model everything it needs up front and it will guess less, ground more, and reason forward from your premises rather than inventing them.
Set reasoning steps and output format
Specify how to think (reason step by step, list assumptions first, verify each step), then fix the output format — usually "show the reasoning first, then give the final answer". This pairing is exactly what makes the model unfold a chain of thought instead of jumping to a conclusion.
Copy into DeepSeek / Qwen
Click Copy and paste the assembled CoT prompt into DeepSeek, Qwen, Doubao or Kimi. Tip: native reasoning models like DeepSeek-R1 already do chain-of-thought internally, so you can drop "reason step by step" instructions. Everything is assembled locally in your browser; nothing is sent anywhere.
How the Chinese chain-of-thought prompt builder works
Why reasoning before the answer makes a prompt more reliable
When you ask a large language model a question that takes more than one step — a maths word problem, a logic puzzle, a plan with dependencies, a piece of code to debug — the single most effective thing you can do is make it reason step by step before it answers. That is chain-of-thought (CoT) prompting: instead of demanding a bare conclusion, you ask the model to lay out its working — the known conditions, the assumptions, each inference — and only then state the final answer. This builder keeps that structure for you: fill the fields and it joins them into a clean prompt that states the task, supplies the context, sets the reasoning steps, fixes a "reasoning first, answer after" format, and lists the constraints, with each section prefixed by a Markdown-style heading the model can read at a glance.
The reason CoT works is not magic. A model that commits to an answer in its very first tokens has no room to correct course; a model that writes out its reasoning first effectively gives itself more computation and more context before it commits, and each written step constrains the next. On multi-step arithmetic, logic and planning tasks this consistently raises accuracy. It also makes the answer auditable: when you can read the chain of inference, you can see exactly which step went wrong and fix the matching field, rather than re-rolling a black box. A good rule of thumb is to make the task concrete and the reasoning instructions explicit — "list the known conditions first, then verify each step" beats a vague "think carefully".
"A model that answers first and reasons after is just justifying a guess. Ask it to reason first, and the same model often gets the right answer."
Reasoning steps and format separate a guess from a checkable answer
The fields that turn a vague request into a dependable one are the reasoning-step requirements and the output format. Telling the model to "reason step by step, justify each step, and verify before concluding" is what unfolds the chain of thought instead of letting it shortcut to a hunch. Fixing the format — "show the full reasoning, then a clearly labelled final answer" — is what makes the result both reliable and easy to scan. And the constraints field earns its place here too: capping the reasoning length, banning invented figures, and asking the model to say 不确定 when a step is shaky keep a long chain of thought from drifting into confident nonsense. None of this limits the model; it focuses it.
One important caveat: chain-of-thought is not free, and it is not always needed. Native reasoning models such as DeepSeek-R1 and the OpenAI o-series already reason internally, so telling them to "think step by step" is redundant and can even interfere — for those, just state the task and constraints cleanly. And on simple factual questions, forcing step-by-step reasoning only makes answers longer, slower and more expensive. Use CoT where it pays: multi-step reasoning, maths, planning and anything where you need to check the logic. Because the output is structured plain text, the same prompt is portable across every major Chinese model and works on ChatGPT, Claude or Gemini, and because the whole tool runs locally in your browser, you can iterate freely — tweak one field, copy again, test — without anything you type ever leaving your device.
About Chain-of-Thought Prompting — 10 Key Points
Chain-of-thought (CoT) means asking the model to write out its reasoning step by step first, then give the final answer — instead of jumping straight to a conclusion.
On multi-step reasoning, maths and logic problems, asking the model to "reason step by step" often markedly improves accuracy.
A simple "Let's think step by step" triggers zero-shot chain-of-thought and is one of the cheapest prompting tricks there is.
Specifying "show the reasoning first, then the final answer" lets you check how the model reached its conclusion, not just see the result.
Asking the model to "list the known conditions and assumptions first" surfaces hidden premises and cuts down on treating bad assumptions as fact.
Adding "verify each step" instructions effectively makes the model self-check, helping it catch and fix reasoning errors mid-way.
Native reasoning models like DeepSeek-R1 and the OpenAI o-series already do chain-of-thought internally — telling them to "reason step by step" is redundant and can even interfere.
Chain-of-thought makes output longer and burns more tokens; forcing step-by-step reasoning on simple factual questions just slows things down and adds cost.
A few demonstrations (few-shot CoT) — one or two worked examples that include the reasoning — further shape the model's reasoning style and format.
This tool assembles the prompt entirely in your browser — your input is never uploaded, never sent to a model, and never stored.
Frequently Asked Questions
- A chain-of-thought prompt explicitly asks the model to "reason step by step first, then give the final answer", showing the intermediate thinking. Compared with demanding a bare conclusion, it is usually more accurate on maths, logic and multi-step reasoning, and it lets you check whether the reasoning actually holds up.
- No. It simply joins the fields you fill in (task, context, reasoning steps, output format, constraints) into a chain-of-thought prompt using a fixed template, entirely in your browser. It does not call DeepSeek, Qwen or any model, and does not go online. You copy the generated prompt and use it in the model of your choice.
- Mostly not. Reasoning models such as DeepSeek-R1 and the OpenAI o-series already do chain-of-thought internally and reason step by step on their own; telling them to "think step by step" is redundant and can even interfere with their internal reasoning. For these models, just state the task, context and constraints clearly — this tool can still help you structure those fields.
- No. Empty fields are omitted automatically. A task / question alone gives you a usable prompt; adding reasoning-step requirements and a "reasoning first, answer after" output format makes the chain-of-thought more reliable and easier to check.
- DeepSeek, Qwen, Doubao, Kimi, ERNIE and Zhipu all work, as do ChatGPT, Claude and Gemini. Because the output is structured plain text, it is vendor-neutral — paste it into the chat box or the system prompt. The lift from CoT prompting is usually largest on non-reasoning models.
- Putting the reasoning before the answer means the model has "thought it through" by the time it commits to a conclusion, which tends to raise accuracy; you also see each step, making it easy to spot where it went wrong. Reverse the order — answer first, reasoning after — and the reasoning often becomes a post-hoc justification with far less value.
- Yes. Step-by-step reasoning markedly increases output length and token use, and is slower and costlier. There is no need to use CoT for simple factual questions; its value is concentrated in multi-step reasoning, maths, planning and tasks where logic must be checked. You can cap the reasoning length in the constraints field.
- You might write "reason step by step and justify each step", "list the known conditions and assumptions first, then derive", or "verify each step and backtrack if you find a contradiction". These instructions decide how the chain of thought unfolds; the more specific they are, the cleaner and more readable the reasoning.
- No. All assembly happens locally in your browser with plain JavaScript. Nothing you type is sent to any model, server or third party, and nothing is stored.
- Completely free, with no account or sign-up and no usage limit. It runs in your browser and collects no data.
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