The Rise of Prompt Engineering with the Emergence of ChatGPT

With the emergence of ChatGPT, the demand for Prompt Engineering has been growing rapidly. Companies are now willing to pay top dollar to hire prompt engineers, with salaries ranging from $150,000 to $350,000. In this article, we will discuss what prompts are, why they are becoming popular, the main categories of prompts with examples, and how to become a prompt engineer with a study plan and resources to study from.

Before we dive into the specifics of prompt engineering, let's first understand what prompts are. Although large language models such as ChatGPT hold an enormous amount of data, they lack the ability to understand your needs except for the prompts you give them. In other words, they are not wired to ask you questions or to understand the context within which you are communicating with them. Therefore, the quality of the prompt determines the quality of the output.

Prompt engineering is the process of engineering a communication style that is best suited for large language models. It is becoming popular because, at the end of the day, prompts are instructions to reach a desired value. In any business, the goal is to reach this desired value as quickly and efficiently as possible.

For example, if you have an online retail business and you want to figure out the optimal marketing strategy, you cannot just ask, "Give me a marketing strategy for my online retail business." The prompt needs to be engineered so that all the necessary information can be fed to the model in a way that understands them and takes those factors into consideration in their entirety. This reduces the randomness and edge cases and makes the responses more precise and tailored.

Once a certain level of precision is reached, businesses love standard practices, which allow them to control the predictability of the responses on a consistent basis. This is why the demand for reusable quality prompts is increasing.

To become a prompt engineer, you need to build domain knowledge of the field of business that you are working in and have a high-level understanding of how the specific models you are interacting with work. The more experienced you become at this, the better problems you can create for specific use cases.

There are many examples of prompts out there on the internet, but let's look at the main established categories among them.

The first category is simple prompts, which are the ones we are all familiar with. For example, "What is the sum of seven and eight?" The model generates a response based on its understanding of the language and any other relevant background knowledge. This method mostly serves our general use cases as they are typically the style of questions that we search on a regular basis.

The second category is role-playing prompts, which stimulate the model to assume the requested character or persona and respond accordingly. For example, "You are a tech copywriter with ten years of experience, and you will help me create tech posts on Twitter." This method allows you more control over the scenario and generates more targeted and realistic responses.

The final category is shot prompts, which are further subdivided into zero shot, one shot, or few shot prompts. With zero-shot prompts, the model generalizes a response to the question without having specifically trained on the prompt itself. For example, "What is the capital of..."

Prompt engineering is becoming an increasingly valuable skill as large language models such as ChatGPT continue to revolutionize the way we interact with information. With the right training and resources, anyone can become a prompt engineer and tap into the growing demand for this skill set.