ChatGPT 结构化 Prompt 的高级应用技巧

Skills（技能）

说明所需知识和技能。 示例：具备企业管理、品牌战略、财务分析等技能。

Initialization（初始化）

提供起始指引。 示例：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。

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

小结

本文系统介绍了如何通过标识符、属性词和模块化设计优化 Prompt。从标识符功能到属性词选用，再到模块化实例展示，这套思路能帮助用户清晰定义任务框架，提升交互效率和信息管理能力。

以下是一个基于 Python 的简单 Agent 实现示例，展示了如何在代码层面调用 API 并处理响应：

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 if output_queue else 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.")