示例1:在有限制篇幅的条件下总结(Summarize with a word/sentence/character limit) 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_rev...
model="gpt-3.5-turbo"): messages = [{"role": "user", "content": prompt}] response = openai.ChatCompletion.create( model=model, messages=messages, temperature=0, # this is
def get_completion(prompt, model="gpt-3.5-turbo"): messages = [{"role": "user", "content": prompt}] response = openai.ChatCompletion.create( model=model, messages=messages, temperature=0, # this is the degree of randomness of the model's output ) return response.choices[0].message["c...
步骤3: 调整模型参数,如语言风格(正式vs随性)、知识面(广度和深度)、最长生成文本长度等。通过提供样本数据与ChatGPT的Prompt接口进行交互式调参。步骤4:
Prompt Perfect Type 'perfect' to craft the perfect prompt, every time. TL;DR: Prompt Perfect 插件是一个用于优化 ChatGPT 对话的工具,它通过重新构造用户输入的方式,使得 ChatGPT 能更准确地理解和回应,从而提高对话的质量和效率。 使用案例 Q:我想知道经济学的知识。 A:[Used Prompt Perfect] 当然,我可...
and the original history and ophthalmic examination were provided subsequently. the same prompt was provided to chatgpt for all cases. cases were provided nonsequentially to chatgpt; in other words, a new chat session was open for each case, thus, chatgpt was not given an opportunity to learn...
ChatGPT is a large language model developed by OpenAI that exhibits a remarkable ability to simulate human speech. This investigation attempts to evaluate the potential of ChatGPT as a standalone self-learning tool, with specific attention on its efficac
response=get_completion(prompt)print(response) 「策略 2:要求模型提供结构化输出」 对于开发者来说,要求模型提供诸如 HTML 或JSON的格式化输出,有利于构建更加健壮的应用。具体的代码示例如下: 代码语言:javascript 复制 prompt=f""" Generate a listofthree made-up book titles along \withtheir authors and gen...
def prompt(question, answers): """ 生成对话的示例提示语句,格式如下: demo_q: 使用以下段落来回答问题,如果段落内容不相关就返回未查到相关信息:"成人头疼,流鼻涕是感冒还是过敏?" 1. 普通感冒:您会出现喉咙发痒或喉咙痛,流鼻涕,流清澈的稀鼻涕(液体),有时轻度发热。
ChatGPT Prompt Engineerin(一) Summarizing(总结/摘要) 今天我们的重点关注按特定主题来总结文本。 设置参数 import openai openai.api_key ='YOUR_OPENAI_API_KEY' def get_completion(prompt, model="gpt-3.5-turbo"): messages = [{"role": "user", "content": prompt}] ...