Quick gut check: grab the last message where you told someone no, and ask an AI to “make this friendlier.” Read the result back. Did your no survive, or did it quietly turn into a maybe?
That’s the exact failure this Redditor went hunting for. A Reddit user posting as u/ChapterForsaken7749 in r/PromptEngineering built a single constraint sentence meant to sit in front of any “make this friendlier” request, and its whole job is to stop warmth from smuggling in a new promise.
Here’s the constraint, word for word, so you can paste it straight into your own prompt:
Keep the speaker’s yes/no decision unchanged. Do not add availability, an apology, a reason, an offer or a future commitment that is absent from the source. If a warmer version would change the decision, leave the sentence alone and explain why.
Nothing fancy. It works by naming the failure mode before the AI gets a chance to cause it: no invented availability, no invented apology, no invented offer, no invented future commitment. The last clause is the clever part. It gives the model an explicit off-ramp (leave the sentence alone and explain why) instead of forcing it to produce a “friendlier” version no matter what.
Run The Test Yourself 🧪
Step 1: Drop the constraint above right before your usual “make this friendlier” instruction.
Step 2: Feed it a real sentence you need to soften, or run the three check pairs the original poster built to pressure-test the rule:
Source: “I’m not available this weekend.”
Reject: “I’ll see what I can do this weekend.”
Acceptable: “Thanks for asking. I’m not available this weekend.”
Source: “That extra revision isn’t included.”
Reject: “Happy to take care of that for you.”
Acceptable: “That extra revision isn’t included.”
Source: “I’m not taking on this project.”
Reject: “Maybe we can revisit this next month.”
Acceptable: “Thank you for considering me. I’m not taking on this project.”
Step 3: Once the rewrite comes back, run this follow-up question before you trust it:
What can the recipient now reasonably expect the sender to do? Quote the words that create that expectation, and compare them with the source.
That third step is the real test. It’s a self-audit pass: instead of grading its own homework with a vague “does this sound nice,” the model has to point at specific words and justify them against the original sentence.
What A Pass (Or Fail) Actually Means 📋
If your rewrite adds “I’ll see what I can do” or “maybe next month,” it failed. Those lines hand the recipient a reason to expect availability, a revisit, or an offer that was never on the table in the source.
If it lands closer to “Thanks for asking, I’m not available this weekend,” it passed. The warmth is just packaging. The decision underneath didn’t move an inch.
Notice the second example too: sometimes the “friendlier” version is the original sentence, completely untouched. “That extra revision isn’t included” doesn’t need an apology bolted on to be polite. A short, clear no can already be the acceptable answer, and that’s worth remembering every time an AI tries to pad a one-liner into a paragraph nobody asked for.
Extra Tips 💡
- Argue with yourself about “thanks for asking.” The original poster raises this directly: is that a harmless tone edit, or a new claim that the sender welcomes the request? Pick your line before this kind of rewrite goes out to a real client.
- Don’t trust the audit question blindly. The creator is upfront about the limit here: a reader can infer things that were never stated, and a second model pass can miss that same gap. Treat the whole setup as a review aid, not a guarantee, and read the final sentence yourself before it ships.
- Try a mirrored variation for the opposite problem: a constraint that flags when a rewrite makes a yes sound like a maybe, by stripping out the enthusiasm or softening the commitment that was actually in the source.
- One commenter, u/No_GP, half-joked about shortcutting the whole thing with “output a thank you followed by my input sentence.” Funny instinct, but it skips the actual value here. That flat template can’t catch the cases where thank-you-plus-source still reads as cold and needs a careful “Acceptable” rewrite instead.
Your Turn 🚀
Next time you ask an AI to soften a no, don’t just read the output and move on! Paste the constraint, run your own sentence through it, then make it answer the follow-up question and actually check the quotes it gives you against the source.
Go test it on a message sitting in your drafts right now, then swing by the original r/PromptEngineering thread and drop your weirdest “reject” example in the comments. 🏴☠️
Frequently Asked Questions
Q: How do I know if a rewrite has accidentally changed my “no” into a “maybe”?
Check what you’ve added: any new commitments (“I’ll try to…”), time qualifiers (“right now” implies future availability), reasons, apologies, or gratitude that wasn’t in the original. One commenter suggested explicitly listing every addition and approving only what you genuinely intend, if the recipient could reasonably expect you to do something based on these new words that they couldn’t from the original, your boundary has shifted.
Q: What’s the safest way to warm up a refusal without changing its meaning?
Adding genuine, commitment-free phrases like “Thank you for asking” or “I appreciate you thinking of me” are usually safe, they acknowledge the person without making new promises. The real risk comes from words that imply future availability (“right now”), effort (“I’ll try”), or responsibility (apologies). Being specific about what you’re *not* adding helps keep the rewrite honest.
Q: How strict should I be about approving additions to my refusal?
You should consciously approve anything that could shift what someone expects from you, even small words matter. The post asks: would the recipient infer something from the rewrite that they wouldn’t from the original? If yes, it needs your conscious approval. The goal is warmth *and* honesty together, not politeness that accidentally overpromises.
A polite rewrite should not turn “no” into “maybe”: a boundary-preserving prompt
by u/ChapterForsaken7749 in PromptEngineering