# Rendering & Guide Formatting Reference

## `clean_guide_text(text)` function (in `blog_markdown()`)

Strips unwanted artifacts from LLM-generated guide text:

```python
def clean_guide_text(text):
    # Strip all [N] reference numbers
    text = re.sub(r'\[\d+\]', '', text)
    text = re.sub(r'\[N\]', '', text).strip()
    # Strip "主线判断：" prefix
    text = re.sub(r'^主线判断[：:]\s*', '', text)
    # Clean extra whitespace
    text = re.sub(r'\s+', ' ', text).strip()
    return text
```

## Summary section rendering

Type labels map: `{'strong': '强信号', 'medium': '中信号', 'risk': '待验证'}`

Output format per type group:
```
## 总结

**强信号**

- **标题（从text第一句提取）**
  解释内容...

- **标题**
  解释内容...

**中信号**

- **标题**
  解释内容...

**待验证**

- **标题**
  解释内容...
```

Title extraction logic:
1. Try splitting on `：` or `:` — if prefix < 60 chars, use as title
2. Otherwise, split on `。！？` and use first sentence as title

## Title translation (Stage 2a)

Titles are translated from English to Chinese in Stage 2a. Rules:
- Brand names preserved: GPT-5, Codex, Gemini, OpenAI, Meta, etc.
- Technical terms with no good Chinese equivalent: keep English
- Everything else: translate to natural Chinese
- LLM prompt explicitly states: "英文品牌名/模型名保留原样，其余翻译为中文"

## LLM prompt for guide (as of 2026-05-30)

Key instructions to LLM:
- 不要空泛总结（如"行业焦点转向XX"），要指向具体事件
- 不要引用编号如[1][3]，读者看不到对应关系
- 不要建议（"开发者应该..."之类删掉）
- 每条控制在2-3句话以内
- 用大白话，不要学术腔