Using GPT to Accelerate Development: Code Generation, Scripting, and Business Integration with Flutter and APIs

The previous article introduced GPT's forms, history, and significance for AI; this follow‑up focuses on practical usage of GPT in development, showing how to generate code, automate scripts, and combine GPT with JD.com's 618 promotion to increase business value.

Scenario 1: Writing Code – By describing requirements in natural language, GPT can produce executable Flutter code for UI components, even for developers unfamiliar with Dart. The article emphasizes clear specification of language, dimensions, colors, and layout to obtain bug‑free code.

Key points for effective code generation:

Express primary information clearly (language, target, size, color, etc.).

Adopt a product‑manager or designer mindset and use natural language instead of strict technical terms.

Avoid contradictory descriptions, which cause bugs.

Take time to formulate precise, coherent sentences.

Never expose confidential or proprietary information to GPT.

Scenario 2: Writing Scripts – Repetitive tasks such as whole‑project name replacement, image name updates, unit testing, or natural‑language conversion can be automated with GPT‑generated scripts. Example script that builds a simple ASCII view and produces a natural‑language description:

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 illustrates how GPT can quickly produce functional code that would otherwise take hours to write.

Scenario 3: Business Integration – The article outlines how to embed GPT into JD.com's search flow for the 618 promotion. Steps include wrapping user keywords, calling GPT via its API, and presenting AI‑generated recommendations.

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

user_input = input("请输入关键词：")
result = search_keyword(user_input)
prompt = "打开京东网站，618大促活动商品里搜索 {user_input}，并给出其相关介绍"

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}")

By dynamically adding promotional context (e.g., "618大促", "京东20周年庆优惠") to the prompt, the system can deliver tailored recommendations, reducing manual effort and improving conversion rates.

The article concludes that each of these GPT‑driven ideas—guidance, copywriting, reverse recommendation, and after‑sale support—can be independently deployed in hackathons or production, with the 618 promotion example illustrating concrete implementation.