Leveraging GPT for Code Writing, Script Generation, and Business Integration

The article introduces practical ways to employ GPT in everyday development tasks, showing how natural‑language prompts can generate functional code, scripts, and business‑level integrations, with a focus on improving efficiency during large promotional events like JD.com’s 618 sale.

Scenario 1: Writing Code – By describing UI requirements in clear natural language (e.g., dimensions, colors, layout), GPT can produce Flutter/Dart view code for developers unfamiliar with the language. The generated code can be directly used to build simple UI components, illustrating how AI lowers the entry barrier for cross‑platform development.

Scenario 2: Writing Scripts – Repetitive tasks such as bulk class‑name replacement, image‑name updates, or unit‑test scaffolding can be automated. A sample Python script that builds a rectangular view and creates a natural‑language description is provided:

width = int(input("请输入宽度："))
height = int(input("请输入高度："))
color = input("请输入颜色：")

view = '#' * width + '\n'
view += ('#' + ' ' * (width - 2) + '#\n') * (height - 2)
view += '#' * width

description = f"生成一个宽度为{width}，高度为{height}的视图，使用{color}颜色填充。"

print(view)
print(description)

This script demonstrates how GPT can be fed a concise description and return ready‑to‑run code, dramatically reducing development time.

Scenario 3: Business Integration – GPT can be embedded into search and recommendation flows. The article outlines three code sections: (1) a function to fetch product information from JD.com, (2) a request to the OpenAI Chat Completion API, and (3) handling the response to display promotional suggestions. Example snippets:

def search_keyword(keyword):
    url = f"https://search.jd.com/Search?keyword={keyword}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, "html.parser")
    introduction = soup.select_one(".p-parameter").get_text(strip=True)
    return introduction

api_endpoint = "https://api.openai.com/v1/chat/completions"
access_token = "你的access_token"
params = {
    "messages": [{"role": "user", "content": prompt}],
    "temperature": 0.7,
    "model": "gpt-3.5-turbo"
}
headers = {"Authorization": f"Bearer {access_token}", "Content-Type": "application/json"}
response = requests.post(api_endpoint, headers=headers, json=params)

if response.status_code == 200:
    response_text = json.loads(response.text)["choices"][0]["message"]["content"]
    print(f"为您在京东推荐了如下结果: {response_text}")
    print(f"您商品的相关介绍：{result}")
else:
    print(f"error: {response.status_code} - {response.text}")

These snippets illustrate how to wrap user queries with promotional context (e.g., "618大促"), call GPT, and present the AI‑enhanced results to end users.

The article concludes with a checklist for effective GPT usage: clear specification of language and UI parameters, natural‑language phrasing, avoiding contradictory statements, taking time to craft prompts, and never exposing confidential business data to the model.