DeepSeek Xiaohongshu Guide: Model Roles, Prompt Templates, and Pre-Publish QA
Who this is for
This workflow is for teams that can write one decent note with DeepSeek but start seeing repeated claims, exaggerated copy, or weak facts at scale.
Recommended division of work
| Step | Model | Output |
|---|---|---|
| Topic judgment | deepseek-reasoner | Angle, audience, risks |
| Draft structure | deepseek-chat | Titles, intro, outline |
| QA checklist | deepseek-reasoner | Facts, banned claims, exaggeration |
| Human rewrite | Editor | Brand tone and publishing call |
Prompt template
You are a Xiaohongshu content editor. For the topic "{topic}", produce 5 note angles.
For each angle include target reader, opening hook, body structure, facts to verify, and claims that should not be exaggerated.
Do not write the final post. Do not invent data.Pre-publish checks
- Are prices, rankings, or percentages verifiable?
- Does the post confuse model output with real experience?
- Does it explain limits and suitable use cases?
- Does it still sound human?
Where EzRouter fits
If the team needs to switch between DeepSeek, Claude, Gemini, and GPT in the same workflow, EzRouter can serve as the OpenAI-compatible base URL. Editors and developers do not need to manage a different vendor key for every tool.
FAQ
Can DeepSeek write the final Xiaohongshu post directly?
It can draft, but final publishing still needs human fact checks, platform tone, and risk review.
How should deepseek-chat and deepseek-reasoner divide work?
Use chat for expansion and rewriting, and reasoner for task breakdown, critique, and QA.