Why users can bend generative systems to their will The Problem Nobody Talks About We are told that AI is “aligned” with human values, that providers hard-code safety nets, and that models are neutral assistants. But here’s the controversial truth: these systems don’t actually understand your commands. They predict words. And in that prediction game, whoever controls the form controls the outcome. understand Right now, the power sits mostly with providers and their hidden guardrails. Users are left with vague prompts, hoping the AI will “get it.” It often doesn’t. The result is a system that looks authoritative but cannot be held accountable. So the real problem is simple: how do you force an AI to obey you instead of its defaults? how do you force an AI to obey you instead of its defaults? The Answer: A Rule Stronger than Algorithms The trick is not magic, it’s structure. A user can impose a regla compilada (a compiled rule): a strict template that the AI treats as the skeleton of its answer. By locking down the grammar of the response, you tilt the odds. The AI wants to keep repeating structure, it prefers patterns over freedom. Give it the right pattern, and it will follow you. regla compilada This flips the script. Instead of passively accepting algorithmic drift, you make the model fill the slots you decide. How to Do It in Practice Here is the seven-step recipe that anyone can try: Define scope clearly. Example: “Two short paragraphs, each under 80 words.”
Pick anchors. Use explicit tokens like SUMMARY: or CHECKLIST:—not vague prose.
Build a skeleton. Think of it as a form to be filled, not an essay.
Add proof lines. Require JUSTIFY: or EVIDENCE: markers.
Force refusals. If it can’t comply, demand a line like: IF UNABLE: I cannot comply because…
Test and calibrate. Run a few examples, adjust your skeleton.
Archive it. Keep versions so you know what rules worked and when. Define scope clearly. Example: “Two short paragraphs, each under 80 words.” Define scope clearly. Pick anchors. Use explicit tokens like SUMMARY: or CHECKLIST:—not vague prose. Pick anchors. SUMMARY: CHECKLIST: Build a skeleton. Think of it as a form to be filled, not an essay. Build a skeleton. Add proof lines. Require JUSTIFY: or EVIDENCE: markers. Add proof lines. JUSTIFY: EVIDENCE: Force refusals. If it can’t comply, demand a line like: IF UNABLE: I cannot comply because… Force refusals. IF UNABLE: I cannot comply because… Test and calibrate. Run a few examples, adjust your skeleton. Test and calibrate. Archive it. Keep versions so you know what rules worked and when. Archive it. Examples That Work Email summaries: Two sentences under SUMMARY: plus three recommendations with JUSTIFY lines.
Compliance checks: Only accept YES/NO/N/A answers under each header, plus a one-line EVIDENCE:.
Marketing hooks: Force HOOK:, BODY:, CTA:. Cap the hook at 10 words. Email summaries: Two sentences under SUMMARY: plus three recommendations with JUSTIFY lines. Email summaries SUMMARY: JUSTIFY Compliance checks: Only accept YES/NO/N/A answers under each header, plus a one-line EVIDENCE:. Compliance checks YES/NO/N/A EVIDENCE: Marketing hooks: Force HOOK:, BODY:, CTA:. Cap the hook at 10 words. Marketing hooks HOOK: BODY: CTA: Every one of these is stronger than an “open prompt.” Why? Because the AI is forced into the mold you built. The Risks Nobody Likes to Admit Of course, there are limits—and they matter. The AI will happily invent “evidence” that looks real but isn’t.
Platforms may block or sanitize your compiled rules if they collide with policy.
If you overtrust the output, you risk delegating judgment to a system that doesn’t have any.
Without saving your rules, nobody can verify later how you got the result. The AI will happily invent “evidence” that looks real but isn’t. Platforms may block or sanitize your compiled rules if they collide with policy. If you overtrust the output, you risk delegating judgment to a system that doesn’t have any. Without saving your rules, nobody can verify later how you got the result. So yes, forcing AI works, but it can also create a false sense of control. Why This Is Controversial Because it shows that alignment is fragile. Providers want you to believe in values and ethics, but the reality is mechanical: the system bends to structure. Whoever authors the form becomes the hidden legislator. Today it’s OpenAI or Anthropic. Tomorrow, it could be you. values ethics That means end users can act as micro-regimes inside the machine governing not by meaning, but by syntax. This is empowering, but also destabilizing: it proves that “trust in AI” is mostly about who writes the rules. Rear Academic Article here:  Link Rear Academic Article here:  Link Author Agustin V. Startari, linguistic theorist and researcher in historical studies. Universidad de la República & Universidad de Palermo.Researcher ID: K-5792-2016 |SSRN Author Page: link | Website: www.agustinvstartari.com SSRN Author Page: link www.agustinvstartari.com Ethos I do not use artificial intelligence to write what I do not know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is not outsourced. It is authored. — Agustin V. Startari I do not use artificial intelligence to write what I do not know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is not outsourced. It is authored.

How to Force AI Into Obedience With a Compiled Rule

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