Why do you see so many “not X, but Y” sentences in LLM output? Because it’s not about exploring ideas, it’s about taking sides. And we created this monster.
LLMs do one thing: predict the next token (a word or fragment), again and again, mining trillions of human sentences for the most likely move. The data they process is from technical manuals, articles, advice columns, meeting notes, user forums, news stories and the vast patchwork of human language.
Clear answers get published, “take a side” is a default editorial move, and instructional texts rarely revel in ambiguity. Most sentences that endure are shaped by human pattern-makers seeking clarity for others. When an LLM learns, it’s mapping all those preferences and rhetorical habits. High up on that list: binary framing, the practice of splitting a story, argument, or explanation into safe pairs, then nudging the reader where “they ought to go.”
But there’s more: as LLMs are tuned to be “helpful” and “harmless,” human reviewers upvote outputs that are concise, clear, and complete which are precisely the qualities found in either/or, less/more, before/after statements. Edges are sanded down and risks are averaged out. The end result? A model that steers unerringly toward binary conclusions.
For content creators using AI, the default response to curb this behavior is to prohibit binary closure directly in your prompt.
Example of a Typical Anti-Binary Prompt:
Avoid using phrases like ‘not X, but Y.’ Instead, write straight forward sentences and phrases.
That didn’t work, did it? LLMs are experts at dodging prompt-level bans with new language patterns. They are trained on oceans of binary-framed language, so their internal logic instinctively gravitates toward “clear contrasts” and “resolution.” Even when you ban a pattern, the model “smuggles” in similar constructions that serve the same binary function, such as:
We have to go deeper by asking for mapped fields, blended states, and open contradictions. This forces the model further from its defaults. But vigilance is always needed: an AI’s instinct for comfort and closure is resilient, resurfacing unless the core generative process is continually challenged.
This example prompt is designed to fundamentally curb binary tendencies in any writing output. It goes beyond the surface to address the root habit.
Example of a Better Anti-Binary Prompt:
// Anti-Binary Writing Directive
When generating any piece of writing:
1. Do not reduce the subject or argument to a binary, two-sided, or either/or framework.
2. Instead, approach every topic as a landscape of multiple perspectives, overlapping concerns, and intersecting possibilities.
3. For every claim or point made, proactively seek and articulate:
a. Additional viewpoints that complicate or extend the initial idea.
b. Zones of ambiguity, uncertainty, or blending between perspectives.
c. Exceptions, hybrids, or cases that resist easy classification.
4. Avoid language, structure, or conclusions that steer toward clear-cut opposition, forced resolution, or closure.
5. Allow tensions, contradictions, and open questions to remain present in the final output.
6. Conclude with a genuine invitation for further exploration or reflection, leaving room for complexity to persist.
Result:
- Writing that fully resists binary framing, surfaces complexity and multiplicity, and models ongoing inquiry instead of definite partition or closure.
This style of prompting actively compels the AI to engage in multi-dimensional reasoning. By requiring multiple perspectives, examination of overlap and tension, and honest treatment of ambiguity and exceptions, the AI must generate complexity and resist binary shortcuts. This yields content that’s richer, less predictable, and more insightful.
Most AI-generated writing falls into the grooves of binary thinking at the deepest structural level. It’s not enough to ban obvious contrasts like “it’s not X, it’s Y;” better outcomes stem from prompting the model to think beyond binaries altogether. By guiding AI to explore multiple dimensions, creators unlock richer insights and break free from formulaic outputs.
The real leap isn’t about surface control, but about reshaping the logic of how ideas are generated and presented—moving AI from either/or to the full spectrum of possibility.
PS. Yes, that was intentional.
