GLM Prompt Builder

Structure is what makes a GLM prompt reliable

When you prompt 智谱 GLM (Zhipu's 智谱清言), 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, 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 the chat box or the API. The result is the kind of prompt a careful prompt engineer would write by hand, only assembled in seconds — and because GLM follows explicit instructions well, a tidy structure pays off quickly.

The single highest-leverage line is the role. "你是一位资深数据分析师" steers GLM'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. GLM is strong across Chinese and English, so write the prompt in the language you want the answer in — or ask for a bilingual reply when you need both. 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, format and tool guidance separate a demo from a usable agent

The fields people skip and regret are output format and constraints — and, for agents, the tool guidance. The format ("answer in bullet points", "return JSON", "under 200 字") turns rambling prose into something you can use or parse in code; and the constraints — "never invent data", "say 不确定 when unsure", "only call a tool when it is actually needed" — are what make GLM safe to put in front of real users or to wire into an automated workflow. None of this limits the model; it focuses it. For agent and tool-calling tasks especially, naming the available tools, when to call them and when not to is consistently the cheapest way to cut wasted calls and raise reliability.

Because the output is structured plain text, the same prompt is portable: it works in 智谱清言, in the GLM API as a system message, and just as well on DeepSeek, Qwen, ChatGPT, Claude or Gemini. 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 Zhipu, or being stored. Treat the first prompt as a draft: run it in GLM, see where the answer drifts, and tighten the matching field. For reasoning tasks, you can ask GLM to think step by step first and then give only the conclusion, keeping the answer focused. 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.