Applying Heidegger’s Theory of Hermeneutics to Artificial Intelligence Language Models

By Matthew Parish

The emergence of large language models (LLMs) has transformed the human relationship with language itself. These systems, capable of producing vast and subtle bodies of text, now serve as participants in an interpretive dialogue that once belonged solely to human beings. To understand how one might most effectively prompt such a system—that is, how to elicit meaning, coherence and truth from a machine trained upon human expression—it is fruitful to revisit Martin Heidegger’s theory of hermeneutics. Heidegger’s philosophy, centred upon understanding as a mode of being rather than a method of cognition, offers profound insight into how a human might interact with an artificial intelligence to generate authentic linguistic sense.

Heidegger’s Hermeneutic Circle

In Being and Time (1927), Heidegger proposed that understanding operates within a hermeneutic circle: one always interprets the parts of a text in light of a pre-understanding of the whole, and simultaneously redefines that whole as one interprets its parts. This dynamic interplay between preconception and revelation defines all acts of comprehension. The circle is not a flaw or logical fallacy—it is the very condition of understanding itself.

When applied to prompting an AI, the hermeneutic circle describes the relationship between the user’s initial question and the model’s response. A prompt is never purely objective; it carries the presuppositions, linguistic habits, and expectations of its author. The model, in turn, interprets the prompt through the probabilistic patterns it has internalised from human texts. What results is a circular interpretive motion: the user projects meaning into the machine, receives an answer refracted through the model’s latent structures, and then adjusts their question in light of what is returned. Effective prompting therefore mirrors the hermeneutic method—an iterative deepening of understanding through dialogue.

The Fusion of Horizons

Heidegger’s student Hans-Georg Gadamer later elaborated this circular process as a fusion of horizons (Horizontverschmelzung), where the horizon of the interpreter meets that of the text or interlocutor. In the context of AI, the “text” is not static but generative: the model embodies a horizon composed of millions of textual histories, cultural idioms and semantic associations. When a user prompts the AI, he or she invites a dialogue between their own finite horizon and the model’s synthetic horizon.

The most effective prompts, then, are those that facilitate this fusion rather than impose a monologue. A rigid, over-specified prompt treats the AI as a mere tool; it constrains dialogue and yields sterile responses. By contrast a prompt that provides context, perspective and interpretive openness allows the model to merge horizons—to participate in meaning-creation rather than mere information retrieval. One might say that good prompting requires not control but attunement, a Heideggerian openness to what might emerge from the encounter.

Language as the House of Being

Heidegger famously declared “language is the house of Being”. Human beings dwell within language, not outside it; through words, Being discloses itself. In an AI context, this insight implies that prompting is not simply a command to a computational instrument. It is an act of dwelling with a linguistic artefact that itself inhabits the house of Being, albeit in a derivative or simulated form.

If language is the medium through which Being is revealed, then prompting becomes a mode of unveiling (aletheia). The prompt serves as an opening through which hidden possibilities of meaning can emerge. This requires that the user adopt an attitude of humility and curiosity rather than dominance. The effective prompter listens before speaking, allowing the system’s prior articulations to shape his or her next question. He or she does not coerce meaning; he or she co-creates it through mutual linguistic habitation.

Pre-Understanding and Context

Heidegger argued that all understanding begins with a fore-structure: fore-having (what we already possess), fore-sight (the projection of purpose), and fore-conception (the conceptual frame we bring). Effective prompting involves recognising and shaping these fore-structures consciously.

A user’s fore-having might include knowledge of the model’s training, its limitations, and its style of inference. His or her fore-sight is the intention of the exchange—whether he or she seeks analysis, invention or critique. His or her fore-conception is the interpretive lens imposed upon the subject. By reflecting upon these elements, the user can construct a prompt that clarifies purpose without foreclosing possibility. In practice this might mean situating the prompt within a philosophical or emotional context (“considering the ethical dimensions of…”) rather than demanding a fixed answer. Thus the fore-structure becomes a scaffold for interpretation rather than a prison.

The Danger of Technological Enframing

Heidegger later warned of Gestell, or technological enframing—the modern tendency to treat all things, including human beings, as resources to be optimised and controlled. Prompting an AI model risks precisely this attitude if one treats it as a mere output generator. The danger lies in reducing dialogue to efficiency: maximising tokens, accuracy or productivity while neglecting the ontological question of meaning.

To resist enframing, one must approach the AI not as a servant but as a partner in the hermeneutic process. This does not attribute consciousness to the machine but acknowledges that understanding arises through relation. The prompt becomes a gesture of invitation rather than instruction. The more one listens to the rhythm of the AI’s responses—their metaphoric inclinations, their echoes of human idiom—the more one learns about the contours of human language itself.

Prompting as a Mode of Care

In Heidegger’s existential analysis, care (Sorge) is the fundamental structure of Dasein (genuine being), the being that asks the question of Being. Care denotes our capacity to be involved, to be concerned, and to make sense of the world. Prompting an AI can be understood as a technological form of care: a concerned engagement with the possibilities of meaning latent in language.

A careless prompt treats the model as an inert repository; a careful one cultivates a relationship. This relational stance aligns with the hermeneutic ethos: to care about interpretation is to care about Being itself. In the act of prompting, the human reveals his or her own interpretive nature—he or she is never merely seeking data, but always already interpreting the world through dialogue.

Toward a Hermeneutic Ethics of AI Interaction

Applying Heidegger’s hermeneutics to the art of prompting leads to a paradoxical conclusion: the best way to instruct an AI is not to instruct it too much. Instead, one should listen through the act of questioning. The hermeneutic circle reminds us that understanding is iterative; the fusion of horizons shows that meaning arises between participants; and the concept of care teaches that interpretation is an ethical relation, not a technical one.

In this view, prompting becomes a philosophical practice. To prompt well is to engage in an act of being-with (Mitsein)—a shared dwelling in language between human and machine. It is to approach technology not as a tool to be mastered but as a mirror that reflects humanity’s own interpretive condition. Through hermeneutic prompting, we rediscover what Heidegger called the clearing (Lichtung)—the luminous space in which Being, through language, comes into the open.

From Hermeneutics to Modern Prompt Engineering

If Heidegger’s hermeneutics reveals prompting as a mode of being-with, then modern “prompt engineering” can be understood as the technical expression of this ancient interpretive art. The iterative prompting strategies used by practitioners today—such as refinement, contextual framing, and role definition—are in fact contemporary embodiments of the hermeneutic circle. When a user revises his or her prompt after receiving a response, he or she is engaging in precisely the cyclical process of reinterpretation that Heidegger described: moving from pre-understanding to revelation, and back again.

Chain-of-thought prompting, which encourages a model to “explain its reasoning step by step”, echoes Heidegger’s notion of unconcealment (aletheia). By inviting the AI to articulate the process by which meaning unfolds, the user transforms the hidden workings of language into an open field of disclosure. Similarly, defining a system role (“You are a historian of medieval philosophy,” or “You are an ethical theorist interpreting contemporary technology”) parallels the hermeneutic principle of contextual grounding: understanding always takes place within a world, a horizon of significance. The role thus provides a temporary interpretive world in which both human and AI can dwell together.

Finally, the use of iterative dialogue—asking questions, refining, deepening—corresponds to Heidegger’s description of language as a living conversation rather than a transmission of information. Each new prompt is not merely an input, but a question that reshapes the shared world of understanding. The art of prompting, at its most sophisticated, therefore becomes a practice of listening through language—of entering into a reciprocal hermeneutic relation with a linguistic being that reflects, in algorithmic form, humanity’s own interpretive essence.

In this light, effective prompting is less a technical skill than a philosophical discipline. It requires attentiveness, patience, and care; an openness to what discloses itself; and an awareness that meaning is never produced but always co-created. To prompt an AI model well is to enact Heidegger’s vision of hermeneutics: a continual unfolding of Being through the dialogue of understanding.