ChatGPT 结构化 Prompt 的高级应用技巧

Constraints（约束条件）

列出完成任务时需遵循的规则和限制，确保结果准确可靠。避免引入未经指定的假设。

Skills（技能）

说明执行任务所需的专业知识，如企业管理、财务分析等，以支持目标高效完成。

Initialization（初始化）

提供起始指引或初始状态，帮助建立互动情境。例如以'您好，我是您的经营会议助手'开场。

工作流程

梳理具体步骤，确保流程清晰可行：

1. 引导用户描述问题。
2. 生成专家角色与用户交互。
3. 分析结果并总结建议。

自动化调用示例

在实际开发中，我们可能需要批量测试不同 Prompt 的效果。下面是一个基于 Python 的简单多线程调用示例，展示了如何封装 OpenAI API 请求：

import openai, sys, threading, time, json, logging, random, os, queue, traceback

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
openai.api_key = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")

def ai_agent(prompt, temperature=0.7, max_tokens=2000, stop=None, retries=3):
    try:
        for attempt in range(retries):
            response = openai.Completion.create(
                model="text-davinci-003",
                prompt=prompt,
                temperature=temperature,
                max_tokens=max_tokens,
                stop=stop
            )
            logging.info(f"Agent Response: {response}")
            return response["choices"][0]["text"].strip()
    except Exception as e:
        logging.error(f"Error occurred on attempt {attempt + 1}: {e}")
        traceback.print_exc()
        time.sleep(random.uniform(1, 3))
    return "Error: Unable to process request"

class AgentThread(threading.Thread):
    def __init__(self, prompt, temperature=0.7, max_tokens=1500, output_queue=None):
        super().__init__()
        self.prompt = prompt
        self.temperature = temperature
        self.max_tokens = max_tokens
        self.output_queue = output_queue or queue.Queue()

    def run(self):
        try:
            result = ai_agent(self.prompt, self.temperature, self.max_tokens)
            self.output_queue.put({"prompt": self.prompt, "response": result})
        except Exception as e:
            logging.error(f"Thread error for prompt '{self.prompt}': {e}")
            self.output_queue.put({"prompt": self.prompt, "response": "Error in processing"})

if __name__ == "__main__":
    prompts = [
        "Discuss the future of artificial general intelligence.",
        "What are the potential risks of autonomous weapons?",
        "Explain the ethical implications of AI in surveillance systems.",
        "How will AI affect global economies in the next 20 years?",
        "What is the role of AI in combating climate change?"
    ]
    threads = []
    results = []
    output_queue = queue.Queue()
    start_time = time.time()

    for idx, prompt in enumerate(prompts):
        temperature = random.uniform(0.5, 1.0)
        max_tokens = random.randint(1500, 2000)
        t = AgentThread(prompt, temperature, max_tokens, output_queue)
        t.start()
        threads.append(t)

    for t in threads:
        t.join()

    while not output_queue.empty():
        result = output_queue.get()
        results.append(result)

    for r in results:
        print(f"\nPrompt: {r['prompt']}\nResponse: {r['response']}\n{'-'*80}")

    end_time = time.time()
    total_time = round(end_time - start_time, 2)
    logging.info(f"All tasks completed in {total_time} seconds.")
    logging.info(f"Final Results: {json.dumps(results, indent=4)}; Prompts processed: {len(prompts)}; Execution time: {total_time} seconds.")

通过上述模块化设计与代码实践，我们可以清晰地定义任务框架，增强信息可读性，有效提升任务执行的准确性和可靠性。这套思路能帮助用户在实际应用中优化交互流程，实现信息处理的精准化和高效化。