TL;DR: Banning a phrase from your prompt doesn’t kill it, it just relocates the tic somewhere worse. A prompt out of r/PromptEngineering fixes this by rotating through real prose stylists instead of freezing on one substitute word.
Anyone who prompts Claude for long stretches knows the whack-a-mole problem. Flag “load-bearing,” and the model just picks a new crutch to lean on. This neuroscience PhD and self-described former English major, posting as u/Spiny_Echidna_80214 on r/PromptEngineering, hit that exact wall and built something sharper than “knock it off.” When you understand how large language models predict text, you realize that simple avoidance commands fight against the core architecture of the system itself.
Why Banning A Word Backfires
Here’s the part most people miss: telling a model “don’t say X” pulls X closer, not further away. The author calls this reifying that location in word space, and it tracks with what a lot of prompt engineers have noticed the hard way. By explicitly naming the word you hate, you activate the neural pathways associated with it, making it statistically more likely to surface in related contexts.
Swap in one replacement word and you haven’t solved anything either. You’ve just handed the model a new tic to overuse in exactly the same spot. “The most important thing” becomes the next “load-bearing,” showing up in every third sentence until it grates just as hard. It turns your text into a predictable mad-lib, where the reader can clearly see the underlying template.
A few commenters backed this up from their own testing. One noted that AI models are notoriously bad at holding onto negative instructions, so “don’t say X” tends to decay into “say X” the moment context gets long. That’s the actual mechanism behind the whack-a-mole effect, not just a vibe. If you ban “delve” in a ten-page document, by page four the model has forgotten the negative constraint but still heavily associates your topic with the concept of deep exploration, bringing the banished word right back into the spotlight.
The Fix: A Pool, Not A Swap
Instead of one substitute, the prompt builds a rotating pool of alternatives for each flagged phrase. The source list draws from actual prose stylists (Didion, Orwell, Woolf, Baldwin, Sebald, McCarthy, Morrison, McPhee) rather than a thesaurus, so the replacements sound like writing, not synonym-generator output. Asking for Joan Didion brings sparse clarity, while invoking James Baldwin introduces rhythmic intensity. This shifts the entire sentence structure rather than just doing a cheap find-and-replace operation.
The model rotates through that pool every time the phrase would otherwise come up. It also checks in with you the first time you flag something, offering three candidates instead of guessing at your favorite. After that first pick, it keeps surfacing fresh options rather than locking onto whichever one you chose. This dynamic rotation keeps the prose feeling organic and prevents the AI from settling into a lazy groove.
Other commenters pushed back with lighter-weight fixes. One suggested just naming a couple of writers to imitate whenever a tic shows up. Another pointed out you can nudge the model toward different “attractors” with a much shorter prompt. Worth trying first if the full version below feels like overkill for your specific project or if you only need a quick one-off generation.
One reply took a different angle entirely: “load-bearing” doesn’t even register as a tic for them, since most of the time they see it in the wild it’s actually describing walls, trusses, and beams. Fair point. Not every flagged phrase deserves the rotation treatment, only the ones that are genuinely everywhere in your output and actively distract from your core message.
Use Cases
- ✍️ Long-form writing where one AI tic (load-bearing, delve, tapestry) has taken over every draft and ruined the pacing
- 📰 Newsletter or blog work where you want variety across dozens of pieces, not one clever swap that readers will eventually notice
- 💬 Any recurring chat where you keep correcting the same phrase and it keeps sneaking back in to sabotage your workflow
Prompt of the Day
Don’t fix an overused phrase by swapping in one substitute because a single replacement just becomes a new tic. Instead, keep a small rotating pool of alternatives for each phrase I flag, drawn from a genuinely wide range of prose stylists rather than a thesaurus. For example, use Didion, Orwell, Woolf, Baldwin, Sebald, McCarthy, Morrison, McPhee (this list is illustrative, not exhaustive, so do not limit yourself to these). Rotate through the pool each time the phrase would otherwise come up, don’t settle on one.
Example: instead of always reaching for “load-bearing,” vary among “the most important,” “the critical,” “the sine qua non,” “the quintessence.”
When I flag a phrase for the first time, I will say something like “I hate that,” and you should give me three candidate replacements to choose from, as clickable options if the platform supports it, otherwise as a short list I can answer in text. Keep offering fresh candidates each time that phrase’s slot comes up again afterward, rather than freezing onto whichever one I picked first.
That middle example line does a lot of work. It shows the model exactly what “rotating” looks like in practice, so it’s not guessing at the pattern from the instruction alone. Providing concrete examples of variety bridges the gap between abstract instructions and actual performance.
Drop this into your Claude project instructions or ChatGPT custom instructions and flag your worst offender the next time it shows up. By making this a persistent system rule, you train the model to handle its own bad habits automatically. Then head over to the original thread on r/PromptEngineering and see which writers other people are borrowing from to keep their text fresh.
Frequently Asked Questions
Q: Why doesn’t just banning a phrase with a prompt actually work?
Turns out, negative prompts backfire (it’s called the “pink elephants problem”) , the model ends up thinking about what you told it to avoid instead of avoiding it. AI is actually better at following positive guidance, like “write in the style of [author]” or being nudged toward alternative ideas, than at avoiding something.
Q: Instead of asking Claude to come up with synonyms, what should I do?
Some users find it works better to shift the work upstream: research an author known for strong writing in that context, then tell Claude to write in that style for that topic. You’re giving it a direction rather than a task, which tends to be less busywork and more effective.
Q: How do I know if a phrase is a real tic or just being used wrong?
Context matters. “Load-bearing” is perfectly natural when describing actual structural members. It’s more of a tic when it’s being used as a lazy synonym for “most important” , look at how the phrase is actually being used before flagging it.
Q: When Claude keeps reaching for a filler word, how do I fix it?
Instead of correcting it, research authors who’ve written well about that specific topic or context, then give Claude those authors as style models. You’re treating the tic as an “unrefined thinking token” that just needs better guidance, not deletion.
Banning “load-bearing” in your system prompt does not result in a load-bearing solution
by u/Spiny_Echidna_80214 in PromptEngineering