企业官网建设流程全解析

提示工程关键原则：

提示词关键：1.写清楚具体的说明；2.给模型充足的思考时间；3.清楚模型的限制

以下样例均基于deepseek（需要配置deepseek api key）

1.写清楚具体的说明：

（1）使用分隔符清楚地指示输入的不同部分

import os import sys from pathlib import Path from dotenv import load_dotenv from openai import OpenAI if sys.platform == "win32": sys.stdout.reconfigure(encoding="utf-8") # 从本文件所在目录读取 .env load_dotenv(Path(__file__).resolve().parent / ".env") api_key = os.getenv("DEEPSEEK_API_KEY") if not api_key: raise SystemExit( "未检测到 DEEPSEEK_API_KEY。请在同目录的 .env 里填入：\n" "DEEPSEEK_API_KEY=你的DeepSeek密钥" ) # DeepSeek 兼容 OpenAI SDK，只需改 base_url 和模型名 client = OpenAI( api_key=api_key, base_url="https://api.deepseek.com", ) def get_completion(prompt, model="deepseek-chat"): messages = [{"role": "user", "content": prompt}] response = client.chat.completions.create( model=model, messages=messages, temperature=0, # this is the degree of randomness of the model's output ) return response.choices[0].message.content text = f""" You should express what you want a model to do by \ providing instructions that are as clear and \ specific as you can possibly make them. \ This will guide the model towards the desired output, \ and reduce the chances of receiving irrelevant \ or incorrect responses. Don't confuse writing a \ clear prompt with writing a short prompt. \ In many cases, longer prompts provide more clarity \ and context for the model, which can lead to \ more detailed and relevant outputs. """ prompt = f""" Summarize the text delimited by triple backticks \ into a single sentence. ```{text}``` """ response = get_completion(prompt) print(response)

得到输出：Clear and specific instructions, even if longer, are essential for guiding a model to produce relevant and accurate outputs, as they provide the necessary context and reduce errors.

（2）要求结构化的输出

text = f""" """ prompt = f""" 输出三本书的名称，和它的作者，以及类型\ 用JSON格式，四个关键词，书籍号，书名，作者，类型。 ```{text}``` """ response = get_completion(prompt) print(response)

得到输出：

```json [ { "书籍号": "978-7-5334-1234-5", "书名": "百年孤独", "作者": "加西亚·马尔克斯", "类型": "魔幻现实主义文学" }, { "书籍号": "978-7-5447-5678-9", "书名": "三体", "作者": "刘慈欣", "类型": "科幻小说" }, { "书籍号": "978-7-5063-9012-3", "书名": "活着", "作者": "余华", "类型": "长篇小说" } ] ```

（3）要求模型先对条件进行检查，当模型做出了假设，要求模型先对假设做出校验。你还可以通过考虑边缘的潜在情况要求模型做出特殊化处理。

（4）在要求模型完成任务之前，提供已经完成的任务实例。

2.给模型充足的思考时间

（1）规定模型完成任务的步骤。

text = f""" In a charming village, siblings Jack and Jill set out on a quest to fetch water from a hilltop \ well. As they climbed, singing joyfully, misfortune struck-Jack tripped on a stone and tumbled \ down the hill, with Jill following suit. \ Though slightly battered, the pair returned home to \ comforting embraces. Despite the mishap, their adventurous spirits remained undimmed, and they continued exploring with delight. """ #example 1 prompt_1 = f""" Perform the following actions: 1 - Summarize the following text delimited by triple \ backticks with 1 sentence. 2 - Translate the summary into French. 3 - List each name in the French summary. Output a json object that contains the following keys: french_summary, num_names. Separate your answers with line breaks. Text: '''{text}''' """ response = get_completion(prompt_1) print("Completion for prompt 1:") print(response)

输出：

Completion for prompt 1: 1 - In a charming village, siblings Jack and Jill set out to fetch water from a hilltop well, but after Jack tripped and tumbled down the hill with Jill following, they returned home battered yet undimmed in their adventurous spirits. 2 - Dans un charmant village, les frère et sœur Jack et Jill sont partis chercher de l’eau à un puits au sommet d’une colline, mais après que Jack a trébuché et dévalé la colline suivi de Jill, ils sont rentrés chez eux meurtris mais avec un esprit aventureux intact. 3 - Jack, Jill. ```json { "french_summary": "Dans un charmant village, les frère et sœur Jack et Jill sont partis chercher de l’eau à un puits au sommet d’une colline, mais après que Jack a trébuché et dévalé la colline suivi de Jill, ils sont rentrés chez eux meurtris mais avec un esprit aventureux intact.", "num_names": 2 } ```

（2）要求模型在得出结论前，先得出自己的解决方案。

3.了解模型限制

（1）模型并不了解自己知识的边界，因此它可能会捏造一些信息来进行回答，这种现象被称之为“幻觉”。

较为有效的解决方案：1.要求模型用输入文本中的相关内容进行回答；2.要求模型追溯到自己回答的源文件

提示工程需要迭代：

迭代过程需要找出为什么指令不够清晰，或者为什么它没有给模型足够的时间去思考，让你改进想法，改进提示。并且多次循环，最后得到完美的结果。

迭代过程：
（1）尝试一些方法

（2）分析结果未提供你想要的内容的地方

（3）澄清指令，给予更多思考时间

（4）使用一批示例优化提示词

总结类应用：

#example prod_review = """ Got this panda plush toy for my daughter's birthday, \ who loves it and takes it everywhere. It's soft and \ super cute, and its face has a friendly look. It's \ a bit small for what I paid though. I think there \ might be other options that are bigger for the \ same price. It arrived a day earlier than expected, \ so I got to play with it myself before I gave it \ to her. """ prompt = f""" Your task is to generate a short summary of a product \ review from an ecommerce site. Summarize the review below, delimited by triple backticks, in at most 30 words. Review: ```{prod_review}``` """ response = get_completion(prompt) print(response) #result：The panda plush is soft, cute, and loved \ #by the daughter, but it's small for the price. \ #It arrived early, which was a bonus.

1.可以使用模型生成简洁明了的总结；

2.可以针对特定对象生成业务中更适用于某个群体的摘要；

3.还可以摘出重要信息，而不是仅仅进行总结；

推理类应用：

类似于：提取标签，提取名字，理解文本感情，诸如此类的事情。

大语言模型很擅长提取特定的文本，减轻了传统机器学习中，“提炼数据集→进行机器学习→训练出特殊模型”的负担。只需要对模型进行特定的提示词处理。

转换类应用：

1.翻译

2.转换格式

3.纠正翻译错误，校准原文本和模型生成文本的差异

扩展类应用：

1.让模型扮演助理并生成ai文本时，让用户知道对话是由ai生成的，非常重要

2.使用temperature（模型的探索程度或随机性）变量，temperat=0时，模型的可靠性越高

构建一个自定义聊天机器人：

import os import sys from pathlib import Path from dotenv import load_dotenv from openai import OpenAI if sys.platform == "win32": sys.stdout.reconfigure(encoding="utf-8") # 与 prompt_test 一致：先读本目录 .env，再读上级目录 .env _script_dir = Path(__file__).resolve().parent load_dotenv(_script_dir / ".env") load_dotenv(_script_dir.parent / ".env") api_key = os.getenv("DEEPSEEK_API_KEY") if not api_key: raise SystemExit( "未检测到 DEEPSEEK_API_KEY。请在 .env 里填入：\n" "DEEPSEEK_API_KEY=你的DeepSeek密钥" ) # DeepSeek 兼容 OpenAI SDK client = OpenAI( api_key=api_key, base_url="https://api.deepseek.com", ) DEFAULT_MODEL = "deepseek-chat" def get_completion(prompt, model=DEFAULT_MODEL): """单条用户 prompt，对应图片里的 get_completion。""" messages = [{"role": "user", "content": prompt}] response = client.chat.completions.create( model=model, messages=messages, temperature=0, # this is the degree of randomness of the model's output ) return response.choices[0].message.content def get_completion_from_messages(messages, model=DEFAULT_MODEL, temperature=0): response = client.chat.completions.create( model=model, messages=messages, temperature=temperature, ) return response.choices[0].message.content def chat_loop( system_prompt: str = "You are a helpful assistant.", model: str = DEFAULT_MODEL, temperature: float = 0.7, ): """交互式聊天：维护 messages 历史，循环读取用户输入。""" messages = [{"role": "system", "content": system_prompt}] print("DeepSeek 聊天机器人已启动。输入 quit / exit / q 退出。") print("-" * 40) while True: user_input = input("你: ").strip() if not user_input: continue if user_input.lower() in {"quit", "exit", "q"}: print("再见！") break messages.append({"role": "user", "content": user_input}) response = get_completion_from_messages( messages, model=model, temperature=temperature ) messages.append({"role": "assistant", "content": response}) print(f"AI: {response}\n") if __name__ == "__main__": chat_loop()