Chinese AI Prompt Builder

Structure is what makes a Chinese prompt reliable

When you prompt a Chinese large language model — DeepSeek, Qwen (通义千问), Doubao, Kimi, ERNIE or Zhipu — the quality of the answer depends far more on how you structure the request than on which clever phrase you use. A structured prompt names the assistant's role, states the task, supplies the context, identifies the audience, sets the voice, fixes the output format, and lists the constraints. This builder keeps that structure for you: fill the fields, and it joins them into a clean prompt with a leading "你是…" role line followed by clearly headed sections, each prefixed with a Markdown-style heading the model can read at a glance, ready to paste into any model. The result is the kind of prompt a careful prompt engineer would write by hand, only assembled in seconds.

The single highest-leverage line is the role. "你是一位资深财务分析师" steers the model's viewpoint, vocabulary and depth in one sentence — far more efficiently than a paragraph of adjectives. After the role, the task and context do the heavy lifting: the task says exactly what to produce, and the context gives the model the facts it needs so it guesses less and grounds more. Naming the audience then keeps every answer pitched correctly, whether the reader is a complete beginner or a domain expert. A good rule of thumb is to make each field concrete and specific: instead of "write well", say "write in clear, plain Chinese, no marketing fluff, aimed at a first-time reader".

Constraints and format separate a demo from a usable assistant

The fields people skip and regret are voice, output format and constraints. The tone keeps the writing on-brand; the format ("answer in bullet points", "return a table", "under 200 字") turns rambling prose into something you can use or parse; and the constraints — "never invent data", "say 不确定 when unsure" — are what make an assistant safe to put in front of real users. None of this limits the model; it focuses it. Spending one extra line on what the model must refuse, and when it should admit uncertainty, is consistently the cheapest way to raise answer quality.

Because the output is structured plain text, the same prompt is portable across every major Chinese model and works just as well on ChatGPT, Claude or Gemini. Write it in Chinese when you want natural, idiomatic Chinese answers; the structure travels regardless of language. And because the whole tool runs locally in your browser, you can iterate freely — tweak one field, copy again, and test — without anything you type ever leaving your device, being sent to a model, or being stored. Treat the first prompt as a draft: run it, see where the answer drifts, and tighten the matching field. Two or three rounds of that usually turn a mediocre reply into exactly what you wanted, and you keep a clean, reusable prompt at the end.