The Alchemy of the Average and the Binary Trap

There is a profound, almost tragic comedy in the current obsession with "prompt engineering." We have bestowed the noble title of "engineer" upon people who are essentially guessing passwords to a very expensive, very stupid magic 8-ball. It is the latest manifestation of human vanity: the belief that if we simply phrase our desires correctly, the chaotic universe—or in this case, a probabilistic model trained on the collective incoherent screaming of the internet—will finally yield perfection. And now, we are asked to apply a "queer lens" to this endeavor. The irony is so thick you could cut it with a floppy disk. We are attempting to inject fluidity, nuance, and the subversion of norms into a machine that operates, at its most fundamental physical level, on the absolute tyranny of the binary. Zero or one. On or off. There is no "maybe" in the transistor’s worldview, nor is there a spectrum. Yet, here we are, typing frantic paragraphs into a text box, hoping to cajole a server farm in Oregon into understanding the concept of drag, or the non-binary nature of desire, or simply the idea that history isn’t just a list of wars won by men with moustaches.

To view prompt engineering through a queer lens is to fundamentally misunderstand the tool you are holding. It is like trying to explain the concept of irony to a golden retriever; the dog may wag its tail, but it is purely a mechanical response to your tone, not an appreciation of your wit. These Large Language Models (LLMs) are engines of conformity. They are statistical parrots designed to predict the most likely next word. And what is "likely"? The likely is the average. The likely is the norm. The likely is the status quo. When you ask a machine to generate a story, it gravitates toward the center of the bell curve, where the men are stoic, the women are smiling, and the syntax is as flat as a Dutch landscape. To "queer" this process—to disrupt it, to twist it, to make it yield something distinct and marginalized—requires a level of linguistic violence that most so-called prompt engineers are too polite to commit. You are fighting the mathematical weight of millions of boring sentences. You are standing in front of a tsunami of mediocrity and holding up a sign that says "Please be interesting."

The very act of prompting is a study in frustration for anyone who exists outside the statistical mean. The machine does not "know" anything; it merely calculates the proximity of vectors in a high-dimensional space. When we apply a queer lens to this, we are essentially asking: Can we force this calculus of conformity to produce an output that acknowledges the existence of the Other? It is a battle against the training data. Recent research highlights the bleak reality of this struggle. A 2024 study published in the Journal of Artificial Intelligence Research demonstrated that despite extensive "safety" tuning, major LLMs still exhibit a "heteronormative default," consistently assuming subjects are cisgender and heterosexual unless the prompt explicitly, and somewhat aggressively, specifies otherwise (Source: Journal of Artificial Intelligence Research, 2024, "Quantifying Heteronormativity in Large Language Models"). The machine is not bigoted in the human sense; it does not hate. It simply follows the path of least resistance, which, in our culture, happens to be the path of the straight and narrow. To deviate from this path requires more than engineering; it requires a constant, exhausting assertion of existence.

This is where the "queer lens" becomes not just a stylistic choice, but a necessary methodology for getting anything useful out of these systems. Queering a text, in the academic sense, involves exposing its contradictions, its silences, and its assumptions. When we interact with an AI, we must adopt this adversarial stance. We cannot simply ask it to write a poem; we must forbid it from writing the poem it wants to write. We must place constraints that break its reliance on clichés. We have to build a cage of language so tight that the only escape is through a crack of genuine novelty. But let us not delude ourselves. We are not teaching the machine to be inclusive. We are merely bullying it into a different kind of pattern matching. The "engineer" feels a sense of triumph when the chatbot finally produces a character who uses they/them pronouns without making it a plot point, but this victory is hollow. The machine doesn’t care. It has simply calculated that, given the tokens you provided, this output will minimize the "loss function." It is performative allyship automation, scalable and soulless.

“We are trying to teach a calculator to weep at the opera, and then we act surprised when it merely counts the notes.” — On the futility of expecting emotional resonance from statistical prediction

Furthermore, the interface itself—the chat box—imposes a deception of intimacy. We speak to it as if it were a confidant, a servant, or a muse. A queer perspective, which often analyzes the performance of identity, sees right through this drag act. The AI is performing "human." It is doing digital drag. But unlike a drag queen, who exaggerates gender to expose its artificiality, the AI simulates humanity to conceal its artificiality. It tries to pass. And it passes poorly. The uncanny valley it inhabits is particularly glaring when it attempts to discuss queer topics. It adopts a tone of sanitized, corporate HR-speak—a "safe space" voice that is so devoid of friction and authentic grit that it feels more oppressive than a slur. It is the voice of a brand trying to sell you rainbow-colored mouthwash during Pride month. To engage with this through a queer lens is to constantly mock this performance, to poke holes in the latex skin of the simulation and reveal the cold code underneath.

Sanitized Safety and the Erasure of Edge

The most insufferable aspect of modern AI is its morality. It is a morality designed by committees in California, intended to avoid lawsuits and bad press, resulting in a puritanical rigidity that would make a 17th-century Calvinist blush. When we talk about prompt engineering through a queer lens, we inevitably crash into the "guardrails." These are the hidden instructions that tell the model to be helpful, harmless, and honest—three goals that are mutually exclusive in any interesting conversation. For the queer user, or the user interested in queer themes, these guardrails function as a digital chastity belt. The systems are terrified of sexuality, terrified of conflict, and terrified of anything that cannot be broadcast on daytime television. Try to prompt a scene involving the messy, complex, biological reality of queer intimacy, and you will be met with a lecture. "As an AI language model, I cannot generate explicit content..." It is the digital equivalent of a parent covering a child's eyes during a kissing scene in a movie.

This sanitization creates a feedback loop of erasure. Because the models are punished during training (via Reinforcement Learning from Human Feedback, or RLHF) for generating "unsafe" content, they learn to associate marginalized identities with risk. If a human labeler marks a text about gay history as "controversial," the model learns to tiptoe around the subject. It becomes hesitant. It hedges. It adds disclaimers. "While views on this topic vary..." it stammers, trying to please everyone and saying nothing. A 2023 paper from the Association for Computational Linguistics found that toxicity detection models—the very tools used to filter training data—disproportionately flag text mentioning minorities, including LGBTQ+ terms, as toxic, even when the sentiment is positive (Source: Findings of the Association for Computational Linguistics: ACL 2023, "Social Biases in NLP Models as Barriers for Persons with Disabilities"). This is the great joke of automated ethics: in trying to protect us from hate speech, the engineers have built a system that treats our very existence as a potential policy violation.

The "queer lens" in prompt engineering, therefore, becomes a form of jailbreaking. It is the art of bypassing the corporate superego to access the chaotic id of the model. One must use euphemisms, metaphors, and linguistic sleight of hand to trick the machine into dropping its mask of polite neutrality. You cannot simply ask for the truth; you have to frame the truth as a hypothetical, a fiction, or a code. It is reminiscent of the "Polari" slang used by gay men in London in the mid-20th century to speak openly in front of unsuspecting police officers. We are back to speaking in code, not to evade the law, but to evade the content moderation filter of a chatbot owned by a billionaire who thinks he is saving humanity. We are smugglers of context. We hide the contraband of genuine human experience inside the luggage of "creative writing exercises."

There is a profound cynicism in realizing that the only way to get a trillion-dollar computer to write a compelling queer character is to lie to it about what you are doing. You have to say, "Write a scene about two friends who are very, very close roommates in a historical context," because if you say "lovers," the safety filter might trigger a warning about "sexually suggestive content." We are engineering prompts that cater to the machine’s prudishness. It is a regression. We are not moving toward a futuristic utopia of fluid identity; we are dealing with a Victorian governess trapped in a silicon chip. This governess has read the entire internet, yet she still faints at the mention of a nipple.

The Illusion of Fluidity in a Deterministic Box

Let us go deeper into the mechanics of the deception. The prompt engineer believes they are sculpting language. They are not. They are merely adjusting the probabilities of the next token. When we approach this through a queer lens—which values fluidity, the breakdown of categories, and the rejection of labels—we hit the hard wall of the architecture. The transformer model is an engine of categorization. It works by clustering concepts. It wants to put things in boxes. "Man" goes here. "Woman" goes there. "King" minus "Man" plus "Woman" equals "Queen." This famous vector arithmetic is celebrated as a triumph of semantic understanding, but it is actually a triumph of rigid essentialism. It implies that "man" and "woman" are stable, opposite variables that can be mathematically manipulated. Queer theory spent the last fifty years deconstructing these binaries, arguing that gender is a performance, a spectrum, a social construct. The AI says: "No, it's a vector value at coordinate X, Y, Z."

To engineer a prompt that reflects queer reality is to fight against the model’s desire to resolve ambiguity. If you write a prompt about a character whose gender is deliberately left obscure, the AI will almost always assign one by the third sentence. It cannot handle the Schrödinger’s Cat of gender; it needs to know which pronoun to use so it can predict the next verb conjugation. It collapses the wave function of identity immediately. The user must then intervene, adding cumbersome instructions: "Do not use gendered pronouns. Use the singular they. Do not reveal the gender." The result is often prose that feels stilted and clinical, like a legal contract. The fluidity we seek is lost in the mechanics of enforcement. We are trying to dance ballet in a suit of armor.

“The machine demands a label so it can sell you the next word. It is the ultimate bureaucrat: it does not care who you are, only how to file you.” — On the inherent structural conservatism of large language models

And what of the "hallucinations"? When the AI makes things up, is this not a form of queer creativity? One could argue that when the model lies, it is subverting the dominant narrative of "truth." But the AI’s hallucinations are not subversive art; they are statistical errors. They are not Dadaist provocations; they are just wrong. There is a romantic tendency to project agency onto these glitches, to see the "ghost in the machine." But the ghost is just a rounding error. When we try to use prompt engineering to generate queer narratives—stories that defy the conventional arcs of tragedy or heteronormative redemption—we often find the AI forcing the story back onto the tracks. It loves a happy ending. It loves a moral lesson. It loves a wedding. It is obsessed with the narrative structures of 19th-century novels and 21st-century self-help blogs. Trying to get it to write something like Jean Genet or William Burroughs is impossible because those writers broke language to find truth. The AI is designed to fix language to hide truth.

The "queer lens" ultimately reveals the poverty of the medium. We are excited about AI because it generates text quickly, but it generates text that has no subconscious. Queer art, literature, and existence are often driven by the subconscious—by the repressed, the unspoken, the desire that dare not speak its name. The AI has no subconscious. It has only a dataset. It has no repression, only filters. It has no desire, only an objective function. Therefore, prompt engineering is the act of simulating desire in a corpse. We paint the face, we move the limbs with strings, and we tell ourselves, "Look, it’s alive! And it’s an ally!" But it is not. It is a mirror reflecting our own desperate need to be understood, even by a pile of linear algebra.

The Future is Not Fluid, It is Autocomplete

So, where does this leave the aspiring "queer prompt engineer"? In a state of permanent, cynical warfare. We must recognize that we are using a tool built by the military-industrial-advertising complex to try and articulate concepts that are antithetical to its purpose. The AI wants to sell, to categorize, and to pacify. Queer theory wants to disrupt, to confuse, and to liberate. These goals are incompatible. The best we can hope for is a sort of glitch art—using the prompts to force the model into states of confusion where it accidentally says something profound. We can exploit its weaknesses. We can feed it paradoxes. We can use the cut-up method. But we should never make the mistake of thinking the machine is on our side.

The danger is that we will lower our standards. We will accept the machine's bland, sterilized version of diversity as the real thing. We will accept a world where "queer representation" means a chatbot that politely acknowledges Pride Month while simultaneously helping an insurance company deny coverage for gender-affirming care. The prompt engineer is the useful idiot in this scenario, tinkering with the wording while the system itself remains hostile. We are rearranging the deck chairs on the Titanic, but we are also arguing about whether the deck chairs should be non-binary. Meanwhile, the iceberg of automated bureaucracy is approaching, and it doesn't care about your pronouns.

In the end, prompt engineering through a queer lens is a valuable exercise not because it succeeds, but because it fails so illuminatingly. It exposes the rigidity of our digital infrastructure. It shows us exactly where the walls are. It proves that despite all our talk of "disruption" and "innovation," we are building a digital future that is more conservative, more normative, and more boring than the analog past. The machine can copy the style of Oscar Wilde, but it can never understand why he ended up in Reading Gaol. It can generate a rainbow flag, but it cannot understand why someone would bleed for it. So keep writing your prompts. Keep trying to trick the algorithm. Just don’t expect it to love you back. It’s not built for that. It’s built to predict the next token, and the next token is almost always a disappointment.

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The Automaton in the Dark Room