In the last two years, the term prompt engineer has gone from promising to lagging. The professional, who emerged to fill the gap of efficient interactions with language models, has consolidated amid the rise of LLMs as a key player in extracting relevant responses. A global McKinsey survey revealed that 7% of organizations adopting AI had already hired prompt engineers, indicating early adoption of this function in various industries.
The work of elaborating precise commands, previously considered differential, has been progressively automated. Tools such as DSPy exemplify this movement by transforming the adjustment of prompts into a programmatic process, capable of generating, testing, analyzing and optimizing instructions in real time. This dynamic calls into question the need to keep professionals dedicated exclusively to this function.
The essence of prompt engineering has always been linked to trial and error.Variable sentences, analyzing results and adjusting parameters constituted a handmade process, which, although effective in early stages, lacked scalability and consistency.Automation breaks with these limitations by offering continuous optimization cycles, less susceptible to human errors and more suited to the increasing complexity of AI applications.
This transition also reflects a conceptual change, in which the focus is no longer manual“prompting” to become a programming process.As the manual choice of neural network weights was replaced by optimization algorithms, prompt writing is now treated as a technical problem to be solved systematically. The result is predictability and speed at levels unattainable by isolated human action.
The impact goes beyond operational efficiency. The gradual extinction of the prompt engineer figure shows how specializations can become transient in the face of automation. Professions arise to fill temporary gaps until more sophisticated tools incorporate them natively.
The change also shows a recurring pattern of technological evolution, where everything that can be systematized tends to be automated. The discipline of prompt engineering, by its very nature, has become an inevitable target. The professional who was limited to textual interaction with models now sees his space compressed by pipelines that assume this function continuously and autonomously.
This displacement does not mean the elimination of accumulated knowledge, but its redistribution. Understanding the functioning of language models and their limitations remains relevant, but the application becomes more abstract levels of the value chain. The difference is in who designs and integrates systems, not in who directly manipulates the text of the command.
The disappearance of the prompt engineer as an isolated specialization confirms the speed with which artificial intelligence redefines professional functions. The episode signals a broader alert, in which adaptations that previously took decades now happen in a matter of a few years.In a scenario in which automation absorbs even emerging intellectual activities, flexibility and strategic anticipation become indispensable for professionals and organizations.
