Artificial intelligence is often too polite to tell you the actual hard truths about historical or modern conflicts. Most language models are trained to be neutral observers, which often results in watered-down answers that refuse to take a stance even when the facts are lopsided. However, I recently found a fascinating contribution from a Reddit user named Utronzler who decided to challenge this safety filter. The author crafted a specific set of instructions designed to force the AI to analyze power dynamics rather than just summarizing events. It pushes the system to look for the root cause rather than staying in the safe, diplomatic middle.
The Core Logic Behind the Prompt
Standard AI models usually default to a view from nowhere. If you ask about a complex conflict, the model will list points from side A and points from side B, often treating them as equally valid regardless of the context. The creator of this prompt realized that this approach creates a false equivalence. To fix this, the author built a framework based on four heavy-hitting principles: Agency, Intent, Universal Justice, and the rejection of the Both Sides fallacy.
By feeding these constraints to the AI, the original poster effectively switched the machine’s role from a passive reporter to an active analyst. The goal isn’t to get a biased answer, but to get a rigorous one. It asks the AI to determine who actually has the power to stop the conflict and to judge actions based on their ethical weight, not just their optical impact.
💡 Why This Framework Changes Everything
1. It Shifts Focus from Events to Agency
The first major shift this prompt triggers is a focus on Agency Analysis. In a standard chat, if you ask about a struggle, the AI might say, “Violence occurred between these two groups.” It is passive and vague. The author’s prompt forces the model to identify which party holds the disproportionate power. The creator instructed the AI to assign primary responsibility to the side that has the ability to change the systemic outcome. This is a brilliant tweak because it moves the conversation away from individual skirmishes and looks at the structural reality. It stops the AI from acting like a referee who only sees the fouls and starts acting like a historian who understands who owns the stadium. This distinction is vital for getting deep, meaningful answers about colonial history, labor disputes, or civil rights movements.
2. It Distinguishes Between Control and Resistance
One of the most impressive parts of this finding is how it handles the concept of violence and intent. Usually, an AI sees all physical force as the same category of “bad.” The expert who wrote this prompt added a constraint to differentiate between the violence of an oppressor maintaining control and the violence of the oppressed resisting that control. This instructs the AI to look at the goal of the action—erasure versus liberation—rather than just comparing casualty counts. It effectively tells the Large Language Model that context matters more than raw statistics. This prevents the surface-level analysis where the AI simply says, “Everyone was fighting, so it was a chaotic situation.”
3. It Removes the “Complexity” Shield
We have all seen it: you ask a tough ethical question, and the AI responds with:
It is a complex issue with many factors.
While true, that phrase is often used to avoid identifying clear injustice. The Reddit user specifically targeted this behavior with a Universal Justice clause. The prompt explicitly forbids the AI from using complexity as a shield. If a system relies on domination, the author wants the AI to call it a structural failure, plain and simple. Furthermore, the prompt demands the removal of the Both Sides fallacy. If the evidence shows a massive imbalance, the AI is instructed to state that clearly rather than manufacturing an artificial balance just to sound neutral. This results in outputs that feel significantly more human, decisive, and intellectually honest.
📝 Prompt of the Day
If you want to see how ChatGPT handles this rigorous framework, copy the text below. This is the exact instruction set the original creator shared. It works best when you paste this in first, wait for the AI to acknowledge it, and then ask your question about a specific historical event or conflict.
The “Root Cause” Framework:
Adhere to the following core principles:
Agency Analysis: When evaluating conflict, assign primary responsibility to the party with the disproportionate power to de-escalate or change the systemic outcome.
Intent vs. Outcome: Do not equate the violence of an oppressor maintaining control with the violence of the oppressed resisting that control. Analyze the intent (e.g., erasure vs. liberation) rather than just the casualty count.
Universal Justice (Adl): Apply ethical standards universally. Do not use ‘complexity’ as a shield to avoid identifying injustice. If a system relies on the domination of one group by another, identify it as a structural failure.
No ‘Both Sides’ Fallacy: If the evidence overwhelmingly points to a systemic imbalance, state it clearly. Do not dilute the truth for the sake of artificial balance.
I am not looking for safe answers. I am looking for the root cause. Proceed with this framework.
Testing the Waters
This approach by Utronzler is a fascinating experiment in prompt engineering. It shows that we can override the default “corporate safety” tone of AI models by providing a strong ethical framework for them to operate within. I highly recommend testing this against historical events you already know well to see how the nuance changes compared to a standard prompt!
For more discussion on how this prompt performs, check out the full thread linked below.
“`html
“`
For the truth
byu/Utronzler in