Prompt engineering – a new profession in the AI era?

The artificial intelligence revolution is bringing about changes not only in technology, but also in the labor market. Among the new professions, prompt engineering is attracting particular attention—a skill that, according to many experts, may become a key competency of the future. But are we really dealing with a new profession, or rather a universal skill of the 21st century?

What is prompt engineering?

Prompt engineering is the ability to communicate effectively with large artificial intelligence models, such as GPT, Claude, Gemini, or DeepSeek, by precisely formulating commands and questions. Its goal is to obtain the best possible answers that meet our specific requirements and expectations.

A well-designed prompt contains not only the question itself, but also context, expectations regarding the format of the answer, examples, and step-by-step instructions. So it's not programming in the traditional sense—it's more like the art of communicating clearly with a machine using natural language.

It is also important to distinguish between two concepts here. A prompt is a specific piece of text entered by the user in natural language to obtain a response from the AI model. Prompt engineering, on the other hand, is a set of rules and techniques to be followed when creating these prompts—it includes optimizing length, structure, context, and format to maximize the effectiveness and repeatability of results.

Basic prompting techniques

To fully understand how to write prompts and become an effective prompt engineer, it is worth learning the main prompting techniques and strategies.

Zero-Shot Prompting

Zero-shot prompting is an approach in which AI undertakes a task based solely on the instructions it receives, without any examples or patterns. The model works independently here, using the knowledge it has to understand the command and provide a response.

Let's say we're writing an article about new technologies and want AI to explain a complex technical term to us. Instead of showing examples, we simply write the prompt: "Explain the concept of 'machine learning' in a way that is understandable to someone who is not familiar with programming. Use analogies from everyday life."

Zero-shot prompting is ideal for quick, one-off tasks where we don't have time to prepare examples, and our command is precise enough for the AI to understand what we expect from it.

Few-Shot Prompting

Chain-of-Thought Prompting

Few-shot prompting, on the other hand, involves providing the model with a few examples of what we want to achieve—usually 1 to 3 samples. These examples serve as a template that guides the AI toward the desired result.

For example, we want AI to write a short movie review in a specific style. Instead of just saying, "Write a review," we give two examples of reviews of different movies. Then we give AI the command: "Write a similar review for movie X, keeping the same format and style."

AI will generate a review according to a template, for example, with a rating, divided into strengths and weaknesses, and a recommendation. Few-shot prompting is extremely useful in situations where we want a specific style and format.

Chain-of-thought prompting is a technique used when we want AI to reason step by step. Instead of giving the answer right away, the model explains its reasoning in individual stages.

Instead of asking AI, for example, "What is the total cost of purchasing five laptops at $3,000 each if the store offers a 10% discount?", using chain-of-thought prompting, we would phrase it as follows: "Solve this problem by showing each step. First, calculate the total price without the discount, then calculate the 10% discount, and finally subtract the discount from the total price and give the final cost."

This method is particularly valuable for more complex business problems, data analysis, or decision-making, where it is important to understand the logic behind the result, rather than just receiving the final answer.

How to write prompts?

A good prompt cannot be a simple question thrown into a chatbot—it must be planned communication with the AI system, where precision directly translates into the quality of the results. Many people make the mistake of treating interaction with AI like a conversation on the street. In reality, every word, every phrase, and every gap in the instruction affects what we get from the model. A well-constructed prompt is one that eliminates ambiguity and guides the AI in a specific direction.

Here again, it is worth using an example. It has been known for a long time that AI can generate great content for company social media profiles, but it needs to be well articulated first.

It is not enough to write: "Prepare content for social media." We will achieve a better effect by writing: "Create 5 posts on LinkedIn for company X (fintech industry). Each post should address a different aspect of data security. The first post should address the problem, and the others should offer solutions."

Context is also extremely important. The prompt "For the e-commerce industry" will give us completely different results than if we specify the context: "For an e-commerce store selling premium women's clothing. The main target group is women aged 25-45, with a high level of consumption, interested in fashion trends. The competition emphasizes low prices, while we focus on quality and design."

It is also useful to define the framework within which AI is to operate. If we write: "Formal, but not too long," it will mean everything and nothing, but if we specify: "Tone: professional but accessible (no jargon). Length: maximum 200 words. Avoid: overly optimistic promises, technical details, comparisons with competitors. Comply with GDPR and avoid collecting personal data in the call to action," the results will be much more satisfactory.

An effective prompt is not about AI reading our minds, but about logically communicating our requirements to it. If we structure our thoughts in the following order: role → task → context → constraints → format, AI will have a clear picture of what we want from it. And the clearer AI understands our intention, the better and more useful results we will get. This is a fundamental skill in an era where human-machine communication is becoming commonplace.

Finally, it is worth remembering about testing and iteration, because the first results from our seemingly well-prepared prompt may be good – but sometimes they may not be perfect. In such cases, we can issue further commands such as: "Expand point 3," "Change the tone to a more humorous one," or "Add numerical data where possible." This is true prompt engineering – an imperfect instruction at first, but later refined through iteration.

From skills to profession, or rather to the evolution of the role of employees?

Is prompt engineer really a new profession? The answer is not clear-cut. In practice, it is not a new job position, but rather a set of new practices and skills described above and their application in the age of AI.

Nevertheless, there is already a dynamic increase in demand for what we might provisionally call a "prompt engineer." Back in 2023, it appeared in literally a dozen or so job advertisements in the IT industry for various positions, and this year there are already several hundred active offers per month. This is a significant change that indicates growing interest in this skill among companies.

However, it is worth noting that prompt engineering is not limited to programmers—it is a skill for everyone. Marketers, specialists, HR professionals, financial analysts, and business strategists successfully use artificial intelligence in their work. The key to success is the ability to combine technical knowledge with business practice – you just need to learn how to translate the company's goals and challenges into a language that algorithms can understand.

Prompt engineering is a channel of communication between our world and the world of artificial intelligence. Regardless of whether we will talk about it in the future as a separate profession or as a universal skill that everyone should have, one thing is certain—its role in shaping the future of work is undeniable.

Today, we are at a similar transition point to where we were 20-30 years ago, when computers became a standard work tool. Back then, people who knew how to use a computer had a competitive advantage. Those who did not learn lost career opportunities. Today, the same thing is happening, except that instead of "computer skills," we are talking about the ability to communicate effectively with AI. Those who learn early on to use AI-based tools consciously and strategically will be better prepared for the challenges of a rapidly changing job market.