To talk about the origin of prompts, we need to start from a mysterious branch of computer science—natural language processing (NLP). The goal of natural language processing is to enable computers to understand and process human language; it is an important intersection of artificial intelligence and computer science. In the development of NLP, scholars have tried various methods to enable computers to communicate freely with humans. Early NLP mainly relied on rules and pattern matching methods, breaking human language down into many grammatical and syntactic rules; however, this approach often struggled with complex language structures and expressions.
With the emergence and development of machine learning and deep learning technologies, natural language processing has achieved breakthrough advancements. In the early 21st century, the rapid development of big data, computational power, and algorithms created favorable conditions for the prosperity of NLP.
Under various innovations, many new models have emerged, such as recurrent neural networks (RNN), long short-term memory networks (LSTM), large language models (LLM), and autoregressive language models (like Transformers). Among these models, the most influential is the GPT (Generative Pre-trained Transformer) series of large language models developed by OpenAI.
The GPT model is a generative pre-trained model based on the Transformer architecture. As of now, GPT has gone through five versions: GPT-1, GPT-2, GPT-3, GPT-3.5, and GPT-4, with increasingly better performance in executing natural language tasks.
Prompts did not exist from the very beginning; they first appeared in the GPT-3 version. This version introduced a special role called “system,” allowing users to guide the conversation by providing instructions or asking questions at the beginning of the dialogue. Before the GPT-3 version, GPT models were mainly used for single text generation tasks rather than dialogue tasks. Therefore, earlier versions of the GPT model did not have a clear prompt concept, instead training and generating based on the entire input text as context.
GPT-3 introduced the ability to perform dialogue tasks, dividing the conversation into user and system roles and using prompts to guide the dialogue. This approach allows ChatGPT to better understand the context and generate more coherent and targeted responses. Since then, prompts have become an important concept in the ChatGPT model, further developed and improved in subsequent versions.
The core idea of prompts is to guide the pre-trained model to generate expected answers or complete specific tasks by designing appropriate and clever hints. The advantage of this method is that we do not need to conduct extensive customized training on the model, saving a lot of computational resources and training time, while the model has strong generalization ability and adaptability.
As people’s understanding of ChatGPT deepens, it has been applied in various fields. For instance, we can use it to write papers, novels, copywriting, learn English, programming, provide psychological counseling, manage finances, and career planning, among others. Prompts play a crucial role in this, as using different prompts can yield completely different results. In fact, prompts have developed into a specialized discipline and may soon become a college major.
What exactly is a prompt? Simply put, it is a technique for effective interaction with AI (artificial intelligence) models. By designing clever input hints, we can guide AI models to generate expected and targeted outputs. You can think of it as a skill for communicating with an agent, helping us better understand and utilize AI technology to meet various needs. A good prompt should be clear, precise, and effective so that the AI model can accurately understand our needs and provide relevant information. Below is an example of a “conversation” with ChatGPT, where the input information is a prompt.
We received a long result; if we want to simplify the output, we can specify that in the prompt.
From the above example, we can see that when the prompt requests a “brief” output, ChatGPT provides suggestions that are more concise and meet our requirements. If a broad or vague prompt is used, the AI model may not be able to determine our intent, resulting in irrelevant or unsatisfactory answers. Providing a clearer prompt can help the AI model better understand our needs.
Therefore, prompt engineering is the study of how to design and optimize prompts to better guide AI models to output responses that meet our expectations. In practical applications, it usually cannot be achieved overnight and requires continuous adjustments to prompts to obtain satisfactory results. This involves various techniques and strategies, including understanding AI model behavior, designing effective prompts, and evaluating and optimizing output results.
Everyone Should Learn Prompts
Today, ChatGPT has permeated various industries, bringing tremendous changes to our lives and work. Even though it is not very convenient to use in the country, it does not diminish people’s enthusiasm for it. It is no exaggeration to say that the emergence of ChatGPT has ignited the global artificial intelligence field, and 2023 is bound to be a turning point year for AI development.
Not to mention, the efficiency improvements brought by ChatGPT in daily work have greatly exceeded expectations. Using ChatGPT to generate meeting minutes, write weekly reports, create various complex formulas in Excel, and produce PPTs are essential skills for many white-collar workers. In these areas, maximizing ChatGPT’s functionality hinges on the use of prompts. In other words, if you learn how to use prompts, your work efficiency will surely improve significantly.
With the release of GPT-4, ChatGPT has transformed into an “encyclopedia”; we can use it as a smart assistant to ask it professional questions from any industry, quickly understanding or learning cross-domain knowledge, with the key point being how to use prompts. Therefore, learning skills related to prompts can help us leverage ChatGPT to quickly become a “jack of all trades.” Imagine a physician using ChatGPT to write an online consultation software in just one week; isn’t that incredible?
In summary, as ChatGPT matures, various similar AI tools will emerge, inevitably changing our work and lifestyle. As an important part of using such AI tools, the study and learning of prompts is imperative.
Super Practical Prompt Handbook
The emergence of ChatGPT signifies the possibility of general artificial intelligence and provides us with a more convenient, intuitive, and personalized way to access information, promising to play an important role in education, research, consulting, and daily life.
To master ChatGPT and make it better serve our work and life requires some skills and methods. This in-depth guide to prompt engineering is definitely worth reading.
“Efficient Questioning with ChatGPT: Revealing Prompt Techniques” By Li Shiming, Dai Xuan, Zhang Tao
