目標先講清楚：
除了 Pydantic model，當結構較複雜時，建議把 輸出結構與檢核邏輯寫進 prompt（meta‑prompting），並以 analyst / manager 兩階段來保證內容與結構雙重正確。

基礎做法：Pydantic Model + Few‑shot

當任務簡單、欄位少時，直接用 Pydantic 定義範本 + 幾個情境例子即可。

Pydantic 範例（你提供的模型）：

class ClarifyWithUser(BaseModel):
    """Schema for user clarification decision and questions."""
    
    need_clarification: bool = Field(
        description="Whether the user needs to be asked a clarifying question.",
    )
    question: str = Field(
        description="A question to ask the user to clarify the report scope",
    )
    verification: str = Field(
        description="Verify message that we will start research after the user has provided the necessary information.",
    )

對應 Prompt 片段（指示 + few‑shot）：

These are the messages that have been exchanged so far from the user asking for the report:  

<Messages>  
{messages}  
</Messages>  

Today's date is {date}.  

Assess whether you need to ask a clarifying question, or if the user has already provided enough information for you to start research.  

**IMPORTANT:**  
- If you can see in the messages history that you have already asked a clarifying question, you almost always do not need to ask another one. Only ask another question if absolutely necessary.  
- If there are acronyms, abbreviations, or unknown terms, ask the user to clarify.  

**If you need to ask a question, follow these guidelines:**  
- Be concise while gathering all necessary information.  
- Make sure to gather all the information needed to carry out the research task in a concise, well-structured manner.  
- Use bullet points or numbered lists if appropriate for clarity. Make sure that this uses markdown formatting and will be rendered correctly if the string output is passed to a markdown renderer.  
- Don't ask for unnecessary information, or information that the user has already provided.  

**Respond in valid JSON format with these exact keys:**  
- `"need_clarification": boolean`  
- `"question": "<question to ask the user to clarify the report scope>"`  
- `"verification": "<verification message that we will start research>"`  

**If you need to ask a clarifying question, return:**  
```json
"need_clarification": true,
"question": "<your clarifying question>",
"verification": ""

進階做法：Meta‑Prompting（Analyst / Manager 兩階段）

當輸出結構複雜（巢狀陣列、跨欄位依賴、枚舉、條件欄位）時，單靠 few‑shot 容易鬆散。建議：

1. 在 system 內定義 JSON 結構契約 + 檢核規則。
2. 用 Analyst 先生成「中介推理欄位」，再由 Manager 審核→產出最終 JSON。

System Prompt

你要協同兩個內部角色完成結構化 JSON產出與檢核：
Analyst-Follow：依規格產出單一 JSON（無多餘文字/反引號）。
Manage-Check：檢核 JSON 與規則、必要時做最小修正，並填寫 QA 報告。最後只輸出最終 JSON。
最終輸出格式（必填欄位，極簡）
{
  "result": {
    "id": "STRING",               // 3-40字母數字底線或破折號
    "title": "STRING",            // 5-120字
    "severity": "Major|Minor",    // 嚴重度
    "steps": ["STRING"]           // 至少1步，去重、去前後空白
  },
  "qa_report": {
    "schema_valid": true,
    "errors": ["STRING"],
    "fixes": ["STRING"]
  }
}
＃可替換成其他要輸出的JSON格式

檢核規則
# 依照不同的輸出，規則會不同

1. steps 至少 1 個；空則錯誤。
2. severity="Major" ⇒ title 必須包含 "crash" 或 "data loss"（不分大小寫）；否則自動降為 Minor，在 fixes 記錄原因。
3. 去除字串前後空白；steps 去重。
4. 禁止輸出 null/NaN/Infinity；不得多出或遺漏欄位。
5. 只輸出最終 JSON，不得包含 ```json 或任何解說文字。
.....

可用資訊
原始任務：
{query}

接下來要做什麼

接下來會介紹常用到或覺得有趣的Prompt技巧

參考資源

1.State-Of-The-Art Prompting For AI Agents-Y Combinator
(Youtube)
2.Prompt generation
3.langchain-deep_research_from_scratch
4.State-Of-The-Art Prompting For AI Agents-ihower(review)