Building a Chinese Content Pipeline with GLM: From Topic to Draft

Content teams rarely struggle with "can't write"—the real pain is scattered topics, drifting style, and endless rework. Here's the workflow we've run for most of the year: using GLM-series models to chain together topic selection, research, drafting, and quality review. The thinking mode handles reasoning-heavy steps; regular chat mode handles light rewriting. Below is the full process plus two prompt templates you can copy directly.

First, assign the work: which steps get thinking mode

Not every task needs deep reasoning. Our rule of thumb is to split by "cost of error":

• Topic breakdown and angle evaluation: use thinking. A mistake here wastes everything downstream, so it's worth letting the model reason through multiple rounds comparing audience fit and writing cost across angles.
• Research aggregation and summarization: regular mode is fine. Tolerance for error is high here, and thinking just slows things down.
• Drafting and rewriting: mixed. Run the outline through thinking to check logic, then generate the first draft in regular mode for smoother output.
• Quality review and fact-checking: thinking is mandatory. Have it challenge claims paragraph by paragraph and flag anything "lacking support."

On the BigModel API platform, thinking mode is controlled via a parameter toggle, so you can switch per batch when processing topics in bulk—check the official API docs for exact parameter names.

Step one: the topic breakdown template

This template solves "an editor gives a vague direction—how do we turn it into something writable?" The key is forcing structured comparison rather than vague prose:

You are a senior editor on a Chinese tech content team. Break down the following topic:

[Topic direction]: {{paste the editor's raw idea, even a half-sentence}}
[Target readers]: Chinese developers and professional users with engineering background
[Publishing channel]: {{WeChat / tech community / site guides}}

Output:
1. Split the direction into 3–5 concrete sub-topics, each with:
   - A title (under 20 characters, no exaggerated phrasing)
   - A one-sentence core argument
   - Estimated difficulty (low/medium/high) and required source material
2. Pick which sub-topic should be published first, with no more than 3 reasons
3. List common content pitfalls for this topic (factual red lines, overpromising, etc.)

Requirements: no generic background padding—output the breakdown directly.

In practice, having the model self-assess writing difficulty filters out topics that "sound grand but can't sustain 1,500 words."

Step two: the drafting template

The key to drafting is locking down structure, style, and banned phrases so output doesn't drift. Bake your team's writing standards right into the template:

You are a technical content writer. Write a Chinese article per the requirements below.

[Topic]: {{sub-topic title}}
[Core argument]: {{one-sentence argument}}
[Available material]: {{paste research notes, data, citations; write "none" if empty}}

[Structure]
- One lead paragraph, no heading, get straight to the problem
- 3–5 body sections, each with a heading
- One closing paragraph that lands concretely—no boilerplate summary
- Total length 1,200–1,800 characters

[Style]
- Sound like a knowledgeable colleague sharing experience, not lecturing
- Banned words: buzzword filler like "empower," "closed loop," "in today's era," "in summary"
- No superlative claims like "best," "first," "only"
- All data must cite sources; if material is "none," describe methods only and never invent numbers

[Output]
First give the article title and a 60-character summary, then the full text.

Don't send the first draft straight to human editors—run an AI review pass first (a thinking-mode task that challenges each paragraph's claims) to cut most of the rework. For content involving images, GLM-4V can handle visual steps: batch-describing screenshots, checking that text in images matches the draft. We use this more than expected.

Where other models fit

It's not either/or. Our setup: GLM handles Chinese-fluent drafting and long-form reasoning; code examples and multilingual material get routed to coding-strong models as needed. The open-weight release suits deploying high-frequency, batch jobs like quality review on an intranet, keeping sensitive drafts local. Each model's capability boundaries shift with version updates—check the official pages for current details before committing.

The real value of this pipeline isn't the prompts themselves—it's that every step has a clear model tier and acceptance criteria. Once the three-stage rhythm is running—slow thinking on topics, fast drafting, strict review—per-person output doubled without quality dropping. Save those two templates to your prompt library and put them to work tonight.