According to Generative AI statistics, by 2025 10% of data generated globally will be created by artificial intelligence. While you might think that anyone can ask AI to create texts, images, and even code, many people make it their job. Career website Indeed displays almost 4,000 jobs in the USA for the so-called “prompt engineers” or “AI whisperers”.
As generative neural networks become more and more widespread, the art of being able to communicate with them is becoming more important than ever. In this article, you’ll learn about what prompt engineering is, how it works, and even learn a couple of tricks that will help you work with generative AI systems like a pro.
Definition of prompt engineering
Prompt engineering is the process of creating tasks (prompts) for generative artificial intelligence systems. Some examples of generative AI are DALL-E, MidJourney, Copilot, and ChatGPT.
Based on user input, they are able to solve a wide variety of tasks:
- DALL-E. Text generation, image generation, image captioning, translation, summarization, question answering.
- ChatGPT. Text generation, code generation, image captioning, translation, summarization, question answering, chatbot conversations, customer service, personalized recommendations.
- Copilot. Code completion, error detection, code optimization, documentation generation, code review.
- Midjourney. Image generation.
There are also many other generative models that are usually used in-house to solve industry-specific tasks.
While the results provided by generative AI might seem incredible, they are only as good as the prompt that people can get. In order to make AI do things for you, you need to understand exactly what you want and how to communicate it in natural language so the machine would understand it too.
This is why prompt engineering is becoming so important. A prompt engineer can determine what data and what format is needed to train the model, as well as what questions to ask the model to get the best result.
How to write great prompts?
Now let us learn some best practices that can significantly improve the quality of GAI outputs. These techniques are actively used by professional prompt engineers, and now you can use them too.
1. Understand what your task is
The first step is to define for yourself what you’re looking for and understand what format you should choose to solve that task. Don’t type anything yet. Let’s say that you are a prompt engineer and want AI to write a text about prompt engineering for you. Should it be a social media post or a blog article? Is there anything you would like to include? For example, if the idea of your article is that everyone should be a prompt engineer and come work at your company, you need to specifically include that.
2. Give AI a role
Now it is time to open ChatGPT and type in your prompt. The first thing to do is to provide AI with a role it should stick to. For example: “You are a prompt engineer with several years of experience”. This way the model can adapt to the task and choose a communication style that is best fitted for this task. You can also give a model a scenario to adjust its style, for example, “You are explaining prompt engineering to a six-year old. Include funny examples that help them understand the topic better”.
Note: You need this step only if you use ChatGPT or other text chatbots.
3. Define the task
Provide as much detail as you can that are relevant to the task: the format, the length, the particular points you want to cover. For example: “Write an Instagram post for a prompt engineer account explaining how prompt engineering works. Explain why this profession is at the top right now and motivate other people to become prompt engineers”.
If you’re generating an image, try to approach this task as if you were giving a description of your mind picture to another person. Describe thoroughly the objects in the image, style, and mood.
4. Encourage the AI to ask you questions
Engineers encourage the algorithm to ask questions. This way the model doesn’t rush to perform tasks it doesn’t fully understand. This step is necessary to collect additional information.
Sometimes it is also useful to make the model accountable for its mistakes. ChatGPT sometimes generates fake sources, makes factual mistakes, and false assumptions. So it is better to at least be notified that the algorithm might be in the wrong.
Note: You need this step only if you use ChatGPT or other text chatbots.
Applications of prompt engineering
Prompt engineering comes in handy in the areas where having a model to produce exactly what is needed is particularly important.
Marketing and content
Generative AI is widely used to write marketing copy, compose pitches, and illustrate articles. But popular magazine Cosmopolitan was the first magazine to outsource their cover to DALLE and Vogue, a world-leading fashion magazine, has also recently used MidJourney to generate their issue cover. It shows that generative AI technologies are penetrating even the fields where big hopes (and, therefore, big bucks) are put on the image’s success. After all, it is the cover that attracts the readers and makes them want to buy the magazine.
Healthcare
Today virtual assistants that answer patient queries and even diagnose certain medical conditions are widely used, but often lack sophistication. Their capabilities of understanding and generating natural speech are limited. With more advanced speech generation technologies and professional prompt engineering that helps to get from the model exactly what is needed, this area can be improved.
Finance
In finance, there is a constant need to analyze and process a lot of information. Prompt engineering can help to train machine learning models that can predict financial markets and determine optimal investment strategies. ML models are much better than humans at classification and clustering tasks that can be used to determine the risks and potential returns of various investment portfolios. The ability of generative models to understand tasks in human language makes it easier to access the technology for more people.
Engineering
Generative models today are already collaborating with software developers to help them write better code and spend less time on researching possible solutions to the problem. In the future, thanks to new generations of generative models, this task can become even more automated. Based on user request, GPT models will be able to create websites, applications, and software. However, they still need a professional who would explain the task to the model and check the deliverables.
How to become a prompt engineer?
Now that you know what fields need prompt engineers, let us see how you can become a prompt engineer and get a prompt engineering job.
Employers typically require you to have a degree in computer science, software engineering, or computer linguistics. Alternatively, you can have proven work experience in the fields of software development and machine learning.
However, before you rush to take a computer science course to get a prominent job in the future, you must assess some risks. AI programs are evolving fast, and there is no guarantee that prompt engineers will be needed in the future.
Moreover, the requirements to how the prompts are made may change and your skills might not be useful for future generations of models. So instead of focusing on formal education, focus on the actual skills that you need for this job. They include, according to Workable:
- Proven work experience as a Prompt Engineer or similar role
- Strong verbal and written communication skills
- Proficient in programming languages, AI-related tools such as ChatGPT, and data analysis techniques
- Comprehensive understanding of natural language processing, machine learning and AI-generated content development
- Excellent problem-solving and analytical skills
- Ability to collaborate effectively with cross-functional teams
- Relevant training and/or certifications in computer science, AI or a related field
To obtain these skills, you may sign up for courses and certifications and complete pet projects. In this GitHub repository, you will find many useful resources that will help you to dig deeper into how prompt engineering works.
Why do prompt engineers receive so much money?
Prompt engineers receive high salaries because they possess specialized skills and knowledge in software development, artificial intelligence, and machine learning. Their expertise is in high demand due to the growing importance of technology in various industries. Additionally, their work often involves solving challenging problems and creating innovative solutions, which further increases their value in the job market.
The salaries of prompt engineers can vary based on several factors including location, experience, qualifications, and the specific industry or company they work for.
In Europe, the salaries of prompt engineers range from €40,000 to €80,000 per year. However, in countries like Germany, the Netherlands, Switzerland, and the United Kingdom, where technology sectors are strong, salaries tend to be higher, with averages ranging from €50,000 to €100,000. However, the number of available jobs is lower than in the States.
In the United States, prompt engineers generally receive higher salaries compared to Europe due to various factors such as a higher cost of living and increased demand for technology professionals. Bloomberg says the average prompt engineering salary ranges from $175,000 to $335,000 per annum.
It is important to keep in mind that these figures are just general estimates and individual salaries can vary based on numerous factors. It is always recommended to research current salary trends and consult reliable sources or job market reports for more accurate and up-to-date information.
Conclusion
In the future, prompt engineering may become an even more common and efficient tool for creating tasks for machine learning models. With the development of technology and the increase in the amount of data, prompt engineering is a hot role.
If you want to become a prompt engineer, you will need to master programming skills and gain a deeper understanding of machine learning technologies. However, as a result, you can get a high-paying and interesting job and potentially change millions of lives.
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