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The Anti-Robot Prompt
A Minimalist Fix for Better AI
Most “humanizing” prompts are actually making your AI dumber and clogging up its memory with useless rules.
We have all seen it: the double em-dashes (–), the overuse of words like “delve” or “tapestry,” and that flat cadence that signals “machine-written.”
A contributor on the ChatGPT Prompt Genius forum offered a cleaner fix. Instead of long lists of banned words, they suggest a single instruction that nudges the model away from its default habits.
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The Core Strategy: Semantic Avoidance
The poster argues that micro-managing output with formatting rules is the wrong approach. Commands like “do not use em-dashes” or “replace dashes with commas” are fragile and often fail in longer chats. Their alternative is one line added to Custom Instructions (Personalization):
“Avoid common LLM patterns and phrases.”
They claim this works better than specific rules because it targets both punctuation habits (like repeated dashes) and vocabulary habits (like generic buzzwords) at the same time. It also avoids turning your prompt into a rulebook.
Why Negative Prompting Clutters the Context Window
The first insight is that negative prompting quietly wastes resources. Every “don’t” is a constraint the model has to keep checking while generating. The author notes this “clutters the context window for longer sessions.”
Practically, a long list of prohibitions increases overhead and can make the writing stiff. A broad directive reduces instruction load and keeps attention on meaning, not policing. Fewer constraints can produce more natural output.
The Problem with Explicit Replacement Rules
The second insight is that replacement rules drift. Instructions like “replace em dashes with ..” require constant reinforcement and are often forgotten after a few messages. This is a common form of instruction drift as the chat grows and priorities shift.
The poster suggests that a high-level style cue sticks better because it sets an overall direction. It defines what to avoid conceptually, not just what to swap mechanically. That makes it more resilient than brittle formatting commands.
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Leaning Into Predictive Capabilities
The theory is simple: this works because it uses what LLMs already know. Models have absorbed huge amounts of writing and can recognize common “LLM patterns” as a category. By telling the model to avoid that category, you push it toward different parts of its training distribution that read more naturally.
In other words, you’re not banning a symbol or a word. You’re steering the model away from a familiar cluster of default completions. The result is often less filler and fewer signature tells.
How to Apply This Fix
If you want to test the method, use this workflow:
Open Settings: Go to your ChatGPT settings menu.
Find Personalization: Open “Custom Instructions” or “Personalization.”
Input the Command: Paste: “Avoid common LLM patterns and phrases.”
Save and Test: Start a new chat and request a short creative or explanatory piece.
This approach is appealing because it subtracts complexity instead of adding more rules. If you want to see the original debate or share your own results, check the link.
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