ChatGPT开发者指南：优化搜索引擎体验

ChatGPT是一个大型语言模型，可以用于自动生成文本回复。它可以用于构建各种利用程序，如聊天机器人、搜索引擎等。

Prompt工程化是指优化LLM的输入提示，以取得更准确、有用的回答。正确的提示可以引导LLM生成高质量的文本回复。

提示编写基础知识

A. 关键词选择

在编写提示时，选择与搜索意图相关的关键词非常重要。这些关键词应当直接与用户的问题或意图相匹配，并且具有特定的领域知识。

例如，在介绍ChatGPT的特点和利用领域时，我们可以选择关键词：“ChatGPT特点”、“ChatGPT利用领域”，以确保回答直接相关。

B. 发问技能

在编写提示时，使用明确、简洁的语言发问是相当重要的。避免使用模棱两可的问题，确保LLM能够正确理解你的意图。

例如，要了解深度学习在图象辨认中效果好的缘由，可以这样发问：“根据深度学习的原理，解释为何深度学习在图象辨认中效果好。”这样的问题更加清晰，使LLM能够准确理解问题。

Prompt工程化实践

A. 设计适合的提示

1. 将问题转化为指令

   为了引导LLM生成期望的回答，将开放性问题转化为明确的指令是很重要的。

   例如，将“介绍ChatGPT的特点和利用领域”改成“罗列一些ChatGPT的特点和利用领域”。这样的提示将直接唆使LLM提供具体的特点和利用领域。

2. 提供上下文信息

   在提示中提供相关的上下文信息，以帮助LLM正确理解问题。

   例如，将“为何深度学习在图象辨认中效果好？”改成“根据深度学习的原理，解释为何深度学习在图象辨认中效果好。”这样的提示提供了深度学习的原理作为上下文信息，有助于LLM更准确地理解问题。

B. 调试和优化提示

1. 测试提示的效果

   使用区别的提示测试LLM的回答质量。

   选择最符合期望的回答，并进行调剂。不断迭代和优化提示，直到取得满意的结果。

2. 分析毛病的回答

   分析LLM回答中的毛病或不准确的地方。

   修改提示或发问方式，以纠正这些问题。

提高搜索引擎体验的最好实践

A. 提供相关的信息

1. 引导用户提供更多细节

   在回答用户问题时，如有必要，提示用户提供更多关于问题的细节，以取得更准确的回答。

2. 提供相关资源链接

   在回答中提供与问题相关的资源链接，以帮助用户进一步了解。

B. 处理歧义和模糊性

1. 澄清问题

   如果LLM产生了模糊或不肯定的回答，进一步澄清问题，确保LLM理解正确。

2. 引导用户提供更多上下文

   如有必要，提示用户提供更多上下文信息，以帮助LLM提供更准确的回答。

总结

Prompt工程化是提高LLM搜索引擎体验的关键技术。公道设计和优化提示能够引导LLM生成准确、有用的回答。遵守最好实践，提供相关信息和处理歧义可以进一步提升搜索引擎体验。

Introduction

In this blog post, we will explore the power of large language models (LLMs) and how they can be utilized to build innovative applications. By leveraging the OpenAI API, developers can now create applications that were previously either too costly, technically challenging, or simply impossible. Join Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI in this short course as they explain the working principles of LLMs, provide best practices for prompt engineering, and demonstrate how LLM APIs can be applied to various tasks, including summarizing user reviews, sentiment classification, topic extraction, translation, spelling & grammar correction, and automatically writing emails.

Understanding Large Language Models (LLMs)

At the beginning of the course, Isa and Andrew will explain in detail how LLMs work. They will delve into the architecture and mechanisms behind these models, highlighting their ability to process and understand natural language. Through this explanation, learners will gain a deeper understanding of how LLMs can be harnessed for a wide range of applications.

Prompt Engineering Best Practices

One of the key skills learners will acquire in this course is prompt engineering – crafting effective prompts that generate the desired output. Isa and Andrew will introduce two fundamental principles for writing effective prompts, and provide practical tips and techniques for systematically engineering good prompts. This section of the course will provide learners with the necessary skills to maximize the potential of LLMs.

Summarizing User Reviews

Summarizing user reviews is a common task that can greatly benefit from LLMs. In this section, Isa and Andrew will demonstrate how to use the LLM API to summarize user reviews for brevity. They will showcase the steps and methods involved in extracting key information from a large volume of user reviews, and condensing it into concise and informative summaries. This will allow developers to gain comprehensive insights from a vast pool of user feedback in a quick and efficient manner.

Sentiment Classification and Topic Extraction

Leveraging LLMs, developers can also perform sentiment classification and topic extraction on large amounts of text data. Isa and Andrew will showcase how to use the LLM API to determine the sentiment of a given piece of text, such as a customer review or social media post. Additionally, they will demonstrate how LLMs can be used to extract relevant topics from a body of text. These capabilities enable the automation and analysis of sentiment and topic-based tasks, saving valuable time and resources.

Translation, Spelling & Grammar Correction

LLMs can significantly enhance translation services by providing accurate and efficient translations. Isa and Andrew will explain how developers can utilize the LLM API to perform translation tasks, enabling seamless communication across languages. Additionally, they will showcase how LLMs can be employed for spelling and grammar correction, ensuring that written text is error-free and polished. These capabilities can greatly improve the quality and clarity of written content, enhancing communication and understanding.

Automatically Writing Emails

Writing emails can be time-consuming and repetitive. Isa and Andrew will demonstrate how LLMs can automate this process by generating email content based on given prompts. They will showcase how developers can utilize the LLM API to create customized email templates and leverage LLMs’ language generation capabilities to automatically draft emails. This application of LLMs can save considerable time and effort, streamlining email communication.

Building a Custom Chatbot

The course will culminate with a section on building a custom chatbot. Isa and Andrew will guide learners through the process of leveraging LLMs to create a chatbot that can engage in interactive conversations. They will explore the prompt engineering techniques necessary for designing an effective chatbot, highlighting the importance of generating appropriate responses. This section will provide learners with hands-on experience in building and deploying their own AI-powered chatbot.

Conclusion

This course provides developers with the practical knowledge and skills to harness the power of large language models. Learners will gain a comprehensive understanding of LLMs and their applications, along with best practices for prompt engineering. By leveraging the OpenAI API, developers can unlock the full potential of LLMs, enabling them to innovate and create value in diverse fields. Through numerous examples and hands-on exercises, learners will be equipped with the tools to build powerful applications that leverage the capabilities of LLMs.

问题1：甚么是ChatGPT Prompt Engineering for Developers？

答案：ChatGPT Prompt Engineering for Developers是一门针对开发人员的课程，教授怎样使用大型语言模型（LLM）快速构建新的强大利用程序。

• 大纲内容：该课程介绍了编写有效提示的模式和方法，以便与大型语言模型进行交互。
• 详细解释和示例：学生可以学习使用LLM来创建聊天利用程序，掌握如何设计引导模型响应的提示。
• 其他相关信息：课程合适任何人参加，无需先修知识。

问题2：Andrew Ng’s ChatGPT Prompt Engineering Course是甚么？

答案：Andrew Ng’s ChatGPT Prompt Engineering Course是一门不要钱课程，由Andrew Ng与OpenAI合作推出，为开发人员提供高质量的内容。

• 具体解释和例子：该课程介绍了怎样使用大型语言模型（LLM）进行提示工程，以提高ChatGPT的性能。
• 其他相关信息：课程不要钱提供，开发人员可以借此提高自己在ChatGPT领域的能力。

问题3：ChatGPT Prompt Engineering的目的是甚么？

答案：ChatGPT Prompt Engineering的目的是通过特定方式构建输入提示，以引导AI模型的回复。

• 详细解释和示例：在ChatGPT Prompt Engineering中，问题或陈说的方式会对模型的响应产生影响，提示工程就是为了在模型中引导期望的回答。