The Prompt Engineer, a key figure to manage the challenges of artificial intelligence in the enterprise

For the past year or so, we have been witnessing the emergence of the Prompt Engineer, one of the currently most in-demand and relevant professions, driven by the exponential spread of artificial intelligence (AI) based tools in the enterprise, such as Midjourney, Jasper.AI and Copilot. Indeed, the rapid development of AI is fostering a revolution in business processes and thus also in the labour market: according to the World Economic Forum report, 'Jobs of Tomorrow: Large Language Models and Jobs', 40 per cent of jobs will be impacted by artificial intelligence within the next five years. Alongside those who will lose or see their jobs changed or enhanced, new professions will also emerge, such as specialists in artificial intelligence and machine learning, which will grow by an estimated 39%.

Already today, however, we can observe how AI is increasingly in demand in the skills pool of workers. Compared to the past, in fact, AI-based tools can support any type of business in achieving competitive advantages by offering increasingly efficient and customised services in a short timeframe. As a result, companies feel an increasing need to hire employees with advanced digital skills and highly specialised profiles in IT and AI.

For instance, in Italy, advertisements mentioning artificial intelligence have increased almost fivefold in the last two years, and applications for these positions have grown by 31 per cent, according to the LinkedIn Global Talent Trends Report.

In this scenario, the figure of the Prompt Engineer is standing out due to its importance in the GenAI context. This new profile represents a key figure in the management of AI-based tools that are gaining widespread popularity in businesses, as he or she has the task of translating human requests into a language that artificial intelligence can understand, in order to extract maximum value from the new generative AI services. It is a role that acts as a bridge between scientific and humanistic disciplines, requiring both linguistic and psychological knowledge and machine learning and programming skills.

Specifically, this is a person who has programming knowledge and knows how to work with large language models (Llm, those underlying solutions such as ChatGpt and Bard). It deals with Natural Language Processing (NLP) projects and trains AI algorithms by providing sets of conversational texts, such as descriptions, contexts, constraints and specific rules, so that these tools can generate increasingly precise answers. Within the scope of services, activities include, for instance, the implementation of effective prompts for language models such as GPT-4, performance analysis of existing prompts and their optimisation, continuous research into new developments and technology trends in the field of AI, and finally the ability to work together with other teams in order to share best practices for the optimal use of prompts within different business contexts.