ChatGPT Prompting开发实战(五)

一、如何编写有效的prompt

对于大语言模型来说,编写出有效的prompt能够帮助模型更好地理解用户的意图(intents),生成针对用户提问来说是有效的答案,避免用户与模型之间来来回回对话多次但是用户不能从LLM那里得到有意义的反馈。本文通过具体案例演示解析两个能够帮助写出有效的prompts的基本原则。案例使用来自OpenAI的模型"gpt-3.5-turbo"并调用相关的chat API:

二、编写清晰和有具体的指令(instructions)的prompt

要点描述:

使用分割符来清楚标明模型输入的不同部分,可以使用的分割符包括:```, """, < >, <tag> </tag>, :等等。

prompt示例如下:

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)

打印输出结果如下:

To guide a model towards the desired output and reduce irrelevant or incorrect responses, it is important to provide clear and specific instructions, which can be achieved through longer prompts that offer more clarity and context.

要点描述:

如何请求LLM给出一个结构化的输出,常见的结构化输出格式有JSON,HTML等。

prompt示例如下:

prompt = f"""

Generate a list of three made-up book titles along \

with their authors and genres.

Provide them in JSON format with the following keys:

book_id, title, author, genre.

"""

response = get_completion(prompt)

print(response)

打印输出结果如下:

要点描述:

请求模型检查输入文本是否满足给定的条件。

prompt示例如下(能够满足给定条件):

text_1 = f"""

Making a cup of tea is easy! First, you need to get some \

water boiling. While that's happening, \

grab a cup and put a tea bag in it. Once the water is \

hot enough, just pour it over the tea bag. \

Let it sit for a bit so the tea can steep. After a \

few minutes, take out the tea bag. If you \

like, you can add some sugar or milk to taste. \

And that's it! You've got yourself a delicious \

cup of tea to enjoy.

"""

prompt = f"""

You will be provided with text delimited by triple quotes.

If it contains a sequence of instructions, \

re-write those instructions in the following format:

Step 1 - ...

Step 2 - ...

...

Step N - ...

If the text does not contain a sequence of instructions, \

then simply write \"No steps provided.\"

\"\"\"{text_1}\"\"\"

"""

response = get_completion(prompt)

print("Completion for Text 1:")

print(response)

打印输出结果如下:

prompt示例如下(不能满足给定条件):

text_2 = f"""

The sun is shining brightly today, and the birds are \

singing. It's a beautiful day to go for a \

walk in the park. The flowers are blooming, and the \

trees are swaying gently in the breeze. People \

are out and about, enjoying the lovely weather. \

Some are having picnics, while others are playing \

games or simply relaxing on the grass. It's a \

perfect day to spend time outdoors and appreciate the \

beauty of nature.

"""

prompt = f"""

You will be provided with text delimited by triple quotes.

If it contains a sequence of instructions, \

re-write those instructions in the following format:

Step 1 - ...

Step 2 - ...

...

Step N - ...

If the text does not contain a sequence of instructions, \

then simply write \"No steps provided.\"

\"\"\"{text_2}\"\"\"

"""

response = get_completion(prompt)

print("Completion for Text 2:")

print(response)

打印输出结果如下:

相关推荐
用户691581141651 小时前
Ascend Extension for PyTorch的源码解析
人工智能
用户691581141651 小时前
Ascend C的编程模型
人工智能
成富2 小时前
文本转SQL(Text-to-SQL),场景介绍与 Spring AI 实现
数据库·人工智能·sql·spring·oracle
CSDN云计算2 小时前
如何以开源加速AI企业落地,红帽带来新解法
人工智能·开源·openshift·红帽·instructlab
艾派森2 小时前
大数据分析案例-基于随机森林算法的智能手机价格预测模型
人工智能·python·随机森林·机器学习·数据挖掘
hairenjing11232 小时前
在 Android 手机上从SD 卡恢复数据的 6 个有效应用程序
android·人工智能·windows·macos·智能手机
小蜗子2 小时前
Multi‐modal knowledge graph inference via media convergenceand logic rule
人工智能·知识图谱
SpikeKing2 小时前
LLM - 使用 LLaMA-Factory 微调大模型 环境配置与训练推理 教程 (1)
人工智能·llm·大语言模型·llama·环境配置·llamafactory·训练框架
黄焖鸡能干四碗3 小时前
信息化运维方案,实施方案,开发方案,信息中心安全运维资料(软件资料word)
大数据·人工智能·软件需求·设计规范·规格说明书
3 小时前
开源竞争-数据驱动成长-11/05-大专生的思考
人工智能·笔记·学习·算法·机器学习