AI Generative: Who is a Prompt Engineer and What Do They Do?

The widespread diffusion and use by the general public of tools that use generative artificial intelligence for informative and creative texts, translations and information, images and videos has made it necessary to learn how to communicate effectively with “the machine” through prompts.

by Luigi Simeone, Chief Technology Officer Moxoff

Why is there a need for prompt engineers?

On the one hand, there is a growing idea among the public and in the internal debate within various sectors of the working world that the advent of artificial intelligence could cause the disappearance of many types of jobs in the near future. On the other hand, it is recognized that AI creates a favorable context for new professional figures [14]. One of these is the prompt engineer [16]. To understand this role, it is first necessary to note that with the development of large language models based on machine learning and their diffusion to the general public, the importance of the way in which the user formulates his or her request verbally (the so-called prompt) during a conversation with the system has become particularly evident.

These models are based on a probability distribution of the terms of a certain language, calibrated during the training using the set of documents provided to the machine as examples of contexts of use of such words. In this way, the system generates the output calculated as the most probable in the face of the particular proposition received from time to time as input; therefore ensures the plausibility and reliability of the response, but not its truthfulness with respect to the data on which the learning process was structured. The need for prompts was not a planned feature, nor was it developed. Hence the need, which arose at the end of 2022 with the release of ChatGPT by OpenAI, to create “special teachers”, such as prompt engineers, in order to teach an artificial intelligence algorithm how to decipher human language to produce desired and especially useful results.

Who is a prompt engineer?

A prompt engineer, in any business and professional sector, is a person dedicated to inserting prompts, rules and instructions designed to make the best use of new generative artificial intelligence services. They typically have programming experience and use large language models (LLMs) such as those underlying ChatGPT and Bard (text-to-text prompting) or DALL-E 3 (text-to-image prompting). They must also have a good command of data processing and information extraction, and be proficient in the use of software tools for the creation of digital contents [11]. In addition to these skills, which are independent of the field in which artificial intelligence is to be used, a deep knowledge of both language and content is required in order to be able to adapt the general guidelines for creating effective prompts to the specific context, for example to generate accurate and convincing communication and marketing content, and to critically evaluate the results provided by LLMs in order to identify incorrect or unsupported responses (known as hallucinations). 

Since it is difficult to find all the various skills described in the same individual, it is reasonable to think of forming groups of prompt engineers to integrate the knowledge of the individuals; according to some opinions expressed in the literature  [10], this scenario represents an interesting opportunity also for subjects who are nearing the end of their working life, who have a vast experience of the typical issues of their activity but may have gaps in their knowledge of the latest advances in the field of artificial intelligence.

Screenshot from Google Trends showing the worldwide level of interest in this topic

What does a prompt engineer do?

The knowledge of AI and natural language is necessary for the prompt engineer to provide advanced input texts, specific labels or identify input strategies to train and guide the language model towards the generation of outputs aimed at a very specific goal. In fact, prompt engineers work on natural language processing (NLP) projects and are responsible for creating the input texts (prompts) based on the output required by the company and the project, with the aim of improving the performance of language models.

For example, an online food sales website has thousands of images of fruit and vegetable products, but none of the image metadata describes which vegetables and fruits are in which photos. Asking the language model to show “an image containing red peppers” is linguistically different from asking for “a photo of red peppers”. In the first case, you might get an image of a pepper salad, for example, in the second a photo of red peppers.

Depending on the way a prompt is written, the response of the artificial intelligence model, the effectiveness of the prompt, and the final result that you want to achieve depends on it.

Which companies need a prompt engineer?

At present, large companies are looking for prompt engineers to optimize the competitive advantage of using generative AI. However, small and medium-sized enterprises (SMEs) and start-ups in every field of business from healthcare to retail will increasingly require prompt engineers with expertise in programming, the principles and techniques of machine learning and deep learning, statistical and mathematical concepts and natural language. In practice, in every field of work, from communication to marketing, from industry to sales, prompt engineers are professional figures who have specific skills in the business area in which they are called to work, such as, for example, skills in medical communication, art, law, but also marketing, CRM, and so on.

There are countless practical examples from the last few months that testify to the vertical rise in interest in this professional figure by companies operating in the most diverse fields. To cite some striking cases, Anthropic, which is a startup financed by giants such as Google [7] and Amazon [4], has offered a salary of $335,000; Klarity, which specializes in software for automatic document review, $230,000, while the consulting firm Booz Allen Hamilton $212,000 [8]. As for the healthcare sector, one of the first hospitals to start looking for prompt engineers was the prestigious Boston Children’s Hospital [3], which ranks high in the rankings of the best US hospitals [6].

It should however be emphasized, for the sake of completeness, that according to some opinions [5], the enthusiasm of companies towards this type of role is destined to be transitory because subsequent improvements in the mechanism of interaction with LLMs will make the job superfluous [17], or it may represent an economic bubble linked to the initial phase of introduction of a new technology (Gartner Hype Cycle) for which expectations are created that are higher than what can objectively be expected from its level of development [18] [19].

Ultimately, as for the future, it is worth noting that the possibility of communicating through human language with generative artificial intelligence systems is so recent for the general public (the key moment is, as mentioned, the launch of ChatGPT, which took place on November 30, 2022) that it is extremely difficult to make predictions about the evolution of the phenomenon and what is connected to it, such as the occupation of prompt engineers [16].











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[11] P. Korzynski, G. Mazurek, P. Krzypkowska, and  A. Kurasinski. Artificial intelligence prompt engineering as a new digital competence: analysis of generative AI technologies such as ChatGPT. Artificial intelligence, 11(3), 2023.

[12] B. Meskó. Prompt engineering as an important emerging skill for medical professionals: tutorial. Journal of Medical Internet Research, 25, 2023.

[13] J. G. Meyer1, R.J. Urbanowicz, P.C.N. Martin, K. O’Connor, R. Li, P.‑C. Peng,

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[15] J.D. Zamfirescu-Pereira, R.Y. Wong, B. Hartmann, and Q. Yang. Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages. 1-21, 2023.

[16] T. Teubner, C.M. Flath, C. Weinhardt, W. van der Aalst, and O. Hinz. Welcome to the era of ChatGPT et al. The prospects of large language models. Business & Information Systems Engineering, 65(2), 95-101, 2023.

[17] O.A. Acar. AI Prompt Engineering Isn’t the Future. Harvard Business Review, 2023.