Semiotic Mediation and the Epistemology of Prompt Engineering

Abstract

The article presents the results of a comprehensive philosophical and semiotic analysis of the phenomenon of prompt engineering as a fundamentally new form of cognitive and communicative interaction between humans and Large Language Models (LLMs). The relevance of the study is driven by the rapid integration of generative artificial intelligence into social and cognitive practices, necessitating a re-evaluation of classical categories such as the sign, meaning, and the status of the cognitive subject. The research substantiates the thesis that, under conditions of digital transformation, the prompt loses the characteristics of a purely linguistic utterance and acquires the status of a tool or an "operational sign" cast in an imperative form. Unlike classical text, a prompt does not describe reality but initiates an algorithmic process within the latent space of the language model, transforming a human cognitive inquiry into a computational procedure.

Particular attention is paid to the analysis of "semantic disorientation" resulting from AI’s operation with "empty signifiers" detached from the lifeworld (Lebenswelt). The study explores the epistemological consequences of delegating cognitive functions to algorithms, where the prompt emerges as an index (in Ch.S. Peirce's terminology), marking the entry point into the language model.

The theoretical novelty of the study is defined by the conceptualization of prompt engineering as a form of semiotic mediation. It is argued for the first time that a prompt is not merely a technical instruction but a dynamic tool of mediation that determines the boundaries of the user's epistemological access to the knowledge accumulated by LLMs. Based on the concept of enunciative praxis, the transformation of the thinking subject into a "semiotic agent" is demonstrated, whose role consists in the strategic manipulation of symbolic forms to achieve a result. Furthermore, a logical experiment verifying ChatGPT's syllogisms demonstrates a discrepancy between human understanding of meaning and machine processing of syntactic structures. The risks of losing subjectivity in the process of AI interaction are revealed as human thinking adapts to the logic of algorithmic responses. The research materials can be utilized for further studies in the semiotics of meaning, digital communications, cognitive science, and the philosophy of artificial intelligence.