Prompt Engineering for Chinese AI Models: Best Practices 2026

July 29, 2026 · 10 min read

Chinese large language models — Qwen 3.5, DeepSeek V3, GLM-4 — have reached performance parity with Western models on most tasks. But getting the best results requires understanding their specific strengths, quirks, and optimal prompting patterns. This guide distills battle-tested techniques for maximizing output quality from Chinese AI models through AIWave's unified API.

Understanding the Landscape

Core Principle: Be Explicit

Chinese models tend to be more literal than GPT-4. While GPT-4 might infer your intent from vague instructions, Qwen and DeepSeek perform best with explicit, structured prompts. Don't rely on the model to "figure out what you mean."

❌ Vague (works poorly)

Write something about machine learning.

✅ Explicit (works great)

You are a senior ML engineer writing a technical blog post.
Topic: "Why transformer architectures dominate NLP"
Target audience: Intermediate developers with basic ML knowledge
Length: 800-1000 words
Tone: Professional but accessible
Include: 2 code examples (Python), 1 comparison table
Do NOT include: Marketing language or hype

Technique 1: Structured System Prompts

System prompts carry more weight in Chinese models than in some Western counterparts. Use them to set clear boundaries and formatting rules:

system_prompt = """You are a data analyst assistant for a SaaS company.

RULES:
- Always respond in JSON format with keys: {insight, recommendation, confidence}
- confidence must be a float 0.0-1.0
- If data is insufficient, set confidence below 0.5 and explain why
- Never fabricate data points
- Use concise, professional language

OUTPUT FORMAT:
{
  "insight": "string - the key finding",
  "recommendation": "string - actionable next step", 
  "confidence": 0.85,
  "reasoning": "string - brief explanation of your analysis"
}"""

response = client.chat.completions.create(
    model="qwen3.5-plus",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "Our CTR dropped 15% this week. DAU is stable."}
    ]
)

Technique 2: Chain-of-Thought with Explicit Markers

DeepSeek and Qwen both benefit from explicit thinking markers. Rather than just saying "think step by step," use structured markers:

prompt = """Solve this problem step by step.

[THINKING] - Show your reasoning process here
[ANALYSIS] - Break down the key components  
[VERIFICATION] - Check your answer
[ANSWER] - Provide the final result

Problem: A company has 3 tiers of pricing. Tier 1: $10/mo, 5K users.
Tier 2: $25/mo, 2K users. Tier 3: $50/mo, 500 users. Churn rates are
5%, 3%, and 1% respectively. Calculate expected monthly revenue
loss from churn."""

response = client.chat.completions.create(
    model="deepseek-v3",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.1
)

Technique 3: Few-Shot with Diverse Examples

Chinese models respond well to few-shot examples, but the examples should cover diverse edge cases, not just the happy path:

prompt = """Classify customer support tickets into categories.

Examples:

Ticket: "I can't log in, it says invalid password but I'm sure it's right"
Category: authentication | Priority: high

Ticket: "How do I export my data?"  
Category: how-to | Priority: low

Ticket: "Your app deleted my entire project!!!"
Category: bug-critical | Priority: critical

Ticket: "The new UI is confusing, where did the settings go?"
Category: ux-feedback | Priority: medium

Now classify this ticket:
"My subscription was renewed but I cancelled last month and was charged $99"
"""

response = client.chat.completions.create(
    model="qwen3.5-plus",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.1
)

Technique 4: Temperature Tuning by Task

Temperature matters more for Chinese models than you might expect. Here's a reference table based on extensive testing through AIWave's platform:

Technique 5: Cross-Model Prompt Portability

One advantage of using AIWave is the unified API. Here's how to build a prompt system that works across Qwen, DeepSeek, and GLM with minimal modification:

def create_prompt(task: str, context: str, model: str) -> list:
    """Create model-optimized messages."""
    system = "You are a helpful, precise assistant. Follow instructions exactly."
    
    if model.startswith("qwen"):
        # Qwen benefits from numbered instructions
        user = f"""Task: {task}

Instructions:
1. Read the context carefully
2. Identify key requirements
3. Provide a structured response
4. Verify your output meets all requirements

Context:
{context}"""
    elif model.startswith("deepseek"):
        # DeepSeek excels with thinking chains
        user = f"""{task}

Think through this step by step:
Step 1: Analyze the requirements
Step 2: Plan your approach
Step 3: Execute and verify

Context: {context}"""
    else:
        user = f"Task: {task}\n\nContext: {context}"
    
    return [
        {"role": "system", "content": system},
        {"role": "user", "content": user}
    ]

# Use with any model on AIWave
for model in ["qwen3.5-plus", "deepseek-v3", "glm-4-plus"]:
    msgs = create_prompt("Summarize this report", report_text, model)
    resp = client.chat.completions.create(model=model, messages=msgs)
    print(f"{model}: {resp.choices[0].message.content[:100]}...")

Technique 6: Handling Chinese and Bilingual Prompts

Chinese models have a unique advantage: native-level Chinese understanding. For bilingual applications, you can mix languages strategically:

bilingual_prompt = """分析以下用户反馈并分类。用英文回复。

用户反馈:
1. "这个APP老是崩溃，太垃圾了" → Sentiment: negative, Category: bug
2. "新功能很好用，谢谢团队" → Sentiment: positive, Category: feature
3. "能不能加个暗色模式？眼睛受不了" → Sentiment: neutral, Category: feature-request

现在分析这条:
"充了钱但是会员没到账，客服也联系不上"
"""

response = client.chat.completions.create(
    model="qwen3.5-plus",
    messages=[{"role": "user", "content": bilingual_prompt}],
    temperature=0.1
)

Common Mistakes to Avoid

1. Overly creative system prompts: Chinese models prefer clear, direct instructions over creative roleplay. Save the creativity for content generation tasks.
2. Ignoring output format: If you need JSON, XML, or markdown, explicitly state it. Don't assume the model will guess correctly.
3. Too many instructions: More than 10 explicit rules in a system prompt can confuse the model. Prioritize the 5 most important.
4. Not testing cross-model: A prompt that works on GPT-4 may need adjustment for Qwen. Always test with your target model.
5. High temperature for structured tasks: This is the #1 cause of format-breaking outputs on Chinese models.

Frequently Asked Questions

Do Chinese AI models respond differently to prompts than GPT-4?

Generally no — they follow the same instruction-following paradigms. However, Chinese models often respond better to structured prompts with clear role definitions and step-by-step instructions. They may also be more literal and less likely to "fill in gaps" compared to GPT-4, making explicit instructions more important.

Can I use the same prompts across different Chinese AI models?

Yes, with minor tuning. Prompts written for Qwen 3.5 typically work well on DeepSeek V3 and GLM-4 with 90%+ consistency. The main differences are in how models handle system prompts and tool-calling formats. Using AIWave's unified API lets you test the same prompt across models easily.

What's the biggest prompting mistake specific to Chinese models?

Using overly vague instructions. Chinese models are trained to follow instructions precisely, which means they'll follow vague instructions precisely — often not producing what you intended. Be explicit about format, length, tone, and constraints.