在相同的檢索結果 + prompt 下
分別使用：

• 一個 flan-t5-small
• 一個 flan-t5-base

比較：

• 回答語氣（口語 / 正式 / 結構）
• 回答資訊的完整程度
  實作 :

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

def generate_answer(model_name, context, query):
    print(f"\n===== 測試模型：{model_name} =====")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    # 英文 prompt
    prompt = f"""
You are a helpful assistant. 
Here is the FAQ:

{context}

Question: {query}

Answer in English, short and clear:
"""

    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    outputs = model.generate(**inputs, max_new_tokens=200)

    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    if answer.strip() == "":
        print("⚠️ 模型輸出為空")
    else:
        print("✅ 回答內容：", answer)
    return answer


# 測試資料
context = "If the product has defects, please take a photo and go to the customer service center to fill out a form. We will handle replacement or refund as soon as possible."
query = "What should I do if my product has defects?"

# === 測試 flan-t5-base ===
ans_base = generate_answer("google/flan-t5-base", context, query)

# === 測試 flan-t5-small ===
ans_small = generate_answer("google/flan-t5-small", context, query)

結果 :
===== 測試模型：google/flan-t5-base =====
✅ 回答內容： Take a photo and go to the customer service center to fill out a form.

===== 測試模型：google/flan-t5-small =====
✅ 回答內容： Take a photo and go to the customer service center to fill out a form. We will handle replacement or refund as soon as possible.

比較 :

• flan-t5-base : Take a photo and go to the customer service center to fill out a form.
  • 內容正確：模型成功抓到了 FAQ 的核心資訊
  • 輸出長度較短：只回覆了處理流程的前半段
• flan-t5-small : Take a photo and go to the customer service center to fill out a form. We will handle replacement or refund as soon as possible.
  • 內容完整：除了流程外，還補上了「我們會儘快處理退款或換貨」
  • 語氣更接近 FAQ 原文，而不是壓縮摘要