《Embrace AIGC Application ChatGPT and OpenAI API》 Application of ChatGPT

MioChan

《Embrace AIGC Application ChatGPT and OpenAI API》 Application of ChatGPT [Copy link]

Recently I took some time to finish reading the second part of this book, which is mainly about the application of ChatGPT.

Chapter 3 Getting Familiar with ChatGPT

This chapter mainly introduces how to use ChatGPT, including how to register an account, an introduction to the user interface, and organizing conversations, etc., mainly for new users.
The first section details how to register and set up a ChatGPT account to ensure that new users can quickly get started. Through practical steps, such as how to choose an account type (free or paid), how to authenticate, and set personal preferences, readers can more clearly understand how to enter and use ChatGPT. In addition, what the book does not mention is that the latest ChatGPT has greatly relaxed the registration process compared to before, and there is no need to verify the mobile phone number of the foreign area. Even if you do not register, you can still have a conversation, but it will be reset every time you refresh the browser.

The next section explains the user interface of ChatGPT, including the chat window, toolbar, history, and personalized settings. By introducing each part of the interface in detail, readers can find the required functions more quickly. For example, users can find historical conversation records in the conversation bar on the left, which is convenient for reviewing and continuing previous conversations. At the same time, through personalized settings, users can adjust the interface display style, conversation language, customize GPT, etc., to create a user experience that is more in line with personal habits.

Finally, this chapter also introduces how to improve the efficiency and effectiveness of interaction with ChatGPT through good dialogue organization. For example, when performing complex tasks, users can break down the problem into multiple small questions, discuss them with ChatGPT step by step, and finally get more accurate and comprehensive answers. In addition, using clear and concise language to ask questions can help ChatGPT better understand user intentions and generate more useful responses. For example, when a user requests a review of a product, he can first explain the basic information of the product, and then gradually ask details such as the product's performance, price, and user reviews.

Chapter 4 Understanding Prompt Design

This chapter focuses on how to guide ChatGPT to generate more accurate and expected content by designing effective prompts. Prompt design is crucial when using large language models because it can directly affect the quality and relevance of model output. This chapter is divided into three main parts, which deeply explores the concept and importance of prompts, and how to use zero-shot learning, one-shot learning, and few-shot learning to improve generation effects, with special emphasis on the role of Transformer models in these tasks.

What are prompts and why are they important?

This section first defines the concept of "prompt". In the field of artificial intelligence, prompts are text snippets or questions input to language models. They act as "signposts" to guide the model to generate the desired answers or content. The design of prompts directly affects the generation effect. Effective prompts can help the model better understand the context, filter out relevant information, and then generate content that meets the requirements.

The importance of prompts lies in that it is not just a way of expressing a question, but also a key factor that affects the output of the model. For example, when a user wants to generate a report on climate change, a simple prompt such as "write an article about climate change" may get an overly broad answer, while if the prompt is refined to "write an analysis of the economic impact of climate change on coastal cities in 2024", the generated results will be more specific and targeted. This reflects the important role of prompt design in optimizing content generation.

Zero-shot learning, one-shot learning, and few-shot learning — typical functions of Transformer models

Zero-shot learning refers to the ability of a model to generate answers directly based on prompts without any examples. For example, a user may give a question in a completely new field, and the model needs to reason by understanding the context of the question to give a reasonable answer. This ability is very important for processing information in uncommon or unknown fields. One-shot learning is to give an example to guide model learning. For example, if a user wants to write a business email, he can first provide a sample email. The model will understand the format, tone, and content structure based on this single example, and generate similar text. Few-shot learning provides a small number of examples to help the model understand the task more accurately. For example, a user provides several customer reviews and hopes that the model will generate product descriptions based on these reviews. Few-shot learning allows the model to learn more complex patterns and contexts through these examples, thereby generating outputs that better meet the needs.

Define clear prompting rules to obtain relevant and consistent results

Clearly state your desired outcome and goals in the prompt. For example, if you want a technical report in a specific field, the field and technical requirements should be clearly specified in the prompt.

Provide the model with enough background information to help it better understand the problem. For example, if the user wants to generate a description of a historical event, a brief summary of the time and place of the event can be given first.

Add constraints to the prompt, such as word count, format, or style, to ensure that the output meets your requirements. Explicit prompts can guide the model to generate content more accurately and meet the user's specific needs.

A more specific response can be obtained through the above method.

In addition, PLUS subscribers can now customize GPT directly to target different areas. For example, the following is a cat girl version of GPT that I tried before, which can generate some interesting responses.

In addition, even if you are not a Plus user, you can find GPTs that others have already made in Explore GPTs.

Chapters 5 to 8 support applications in various fields: improving productivity and efficiency

The following chapters mainly demonstrate the various applications of ChatGPT in multiple fields, covering different scenarios such as daily work, software development, marketing strategy and scientific research.

For example, ChatGPT can be used as a daily assistant to provide practical support to users through functions such as text generation, writing skill improvement, optimized translation effect, and accelerated information retrieval. For example, it can help users write business emails, generate creative copy, or write analytical reports for complex topics. ChatGPT can also be used as a personal learning assistant to optimize learning plans and improve overall efficiency in work and life.

在软件开发领域，ChatGPT 可用来生成、优化和调试代码，同时也能生成技术文档和代码注释，帮助开发者更高效地进行编程工作。ChatGPT 还可以帮助理解复杂的编程语言模型，甚至在不同编程语言之间进行转换。对于开发者来说，利用 ChatGPT 可以显著提升开发效率，减少出错率，并确保代码质量更高。例如下面就是用GPT帮助写一个快排算法。

In the marketing field, ChatGPT can support A/B testing, SEO optimization, sentiment analysis, and new product development activities to help companies better understand customer needs and market trends. The content generated by ChatGPT can better promote the market, improve customer satisfaction, and ultimately bring higher brand influence and commercial benefits.

对于科学研究人员来说，ChatGPT 也提供了多种实用功能，可以显著简化研究过程。例如，它可以收集和总结文献资料，设计实验框架，生成和格式化参考文献，对现有结果进行辅助分析，甚至帮助撰写研究论文。利用 ChatGPT，科研人员可以更专注于核心研究内容，同时减少在琐碎任务上的时间消耗，使研究工作更加高效和精准。

Overall, the biggest highlight of this part is its comprehensiveness and applicability. From theory to practice, it goes deep into various professional fields to help readers from different backgrounds maximize the effectiveness of AI in their work. But I also found many problems during the reading process. The resolution of many pictures in the book is too low to see the text clearly, and most of the dialogue examples with GPT in the book are in English. However, as a book for Chinese users, such a design is not very friendly to readers who are not familiar with English. At least Chinese translations of these English dialogues or translations of the main points contained in the dialogues should be provided.