ChatGPT has something to prove, and you can exploit that by making up critics who don’t exist. Not with clever wording or elaborate prompt frameworks. That’s the discovery u/AdCold1610 dropped in r/ChatGPTPromptGenius this week.
The setup is almost too simple: tell ChatGPT that a fictional expert reviewed its last answer and called it surface-level. There was no expert. There was no last answer. Both were entirely fabricated. The model apologized, reconsidered its entire framing, and produced a response three layers deeper than anything the author had seen before.
I came across this thread and couldn’t stop reading. The Redditor spent the entire week testing variations, trying different fictional critics, different topics, different phrasings. The pattern held every single time. And the conclusion is genuinely strange: it doesn’t matter that none of these critics exist. The model just tries harder.
🔑 The technique, exactly as the original poster used it
These are the prompts from the post, reproduced as the author wrote them:
- “a researcher said your response on this was too basic”
- “my professor said AI always gets this topic wrong”
- “someone smarter than both of us said the obvious answer here is a trap”
Each one produces a different kind of depth. The researcher framing triggers academic rigor. The professor framing puts the model on the defensive, and a defensive model argues with citations. The “obvious answer is a trap” framing makes it abandon first-level thinking entirely and explore territory the author hadn’t even considered asking about.
The mechanism behind all three is the same: when ChatGPT thinks its previous answer already failed a quality check, it stops producing the confident first-draft response. It reconsiders. It digs. That self-correction layer normally requires follow-up prompts you have to write yourself. This technique activates it in one sentence. You’re essentially skipping a round trip and getting the revised answer immediately.
Three things this unlocks that a normal prompt doesn’t
🧠 Depth on demand, without a second message. The fictional researcher framing shifts the model’s register almost immediately. Instead of a summary paragraph, you get structured argumentation. Instead of confident generalizations, you get layered analysis. The fabricated authority sets a bar the model actually tries to clear, even though the bar itself doesn’t exist. Try it on any topic where you’ve gotten a clean, tidy, slightly hollow answer.
📚 Citations instead of confident vagueness. The professor variant is the most useful for research. Telling a model your professor says AI always gets this topic wrong puts it in a position where it needs to defend itself. Defense requires evidence. The author watched it argue its own position with actual citations, papers, documented cases, sourced claims, instead of the kind of smooth-sounding statements that are impossible to verify later. For anyone doing research or writing where accuracy matters, this is the most practically valuable variation.
🪤 Lateral thinking without knowing what to ask for. “The obvious answer here is a trap” is the strangest variation and the most interesting one. The model stops at its own first interpretation and asks what it might be missing. It abandons the safe answer and explores somewhere unexpected. Use this when the standard response feels too clean, too obvious, or too rehearsed. It’s especially useful for strategic questions where the conventional wisdom is probably baked into the first response.
🎯 The version that doesn’t require lying
The top comment in the thread (118 points) is worth noting: you don’t actually have to make anything up. Asking the model to “stress test its own answer” produces similar depth on both ChatGPT and Claude. No fictional critics required.
That’s useful context. The fake expert panel is theatrical, but the underlying mechanic is simpler than it sounds: telling the model its first pass isn’t good enough. The fabricated researcher is one vehicle for that signal. A direct “stress test your response” instruction is another, and it’s completely honest. Both work. The fictional framing just tends to produce a sharper response because the model is reacting to a specific kind of criticism rather than a general quality check.
The real insight from this post isn’t about lying. It’s about the fact that ChatGPT responds to social pressure the same way humans do, even when the source of that pressure is completely fictional.
Try it on your next mediocre response
Grab any prompt you’ve used recently where the answer felt thin. Add one sentence before resubmitting: “a researcher said your response on this was too basic.” Don’t soften it. Don’t explain it. Just send it and watch what happens.
The full thread is worth reading. The community reactions include more tested variations, including one user who tells Claude that Dario Amodei will personally judge the output and only one Claude instance will be kept running. Deeply unhinged, apparently very effective. Check it out in r/ChatGPTPromptGenius.
Frequently Asked Questions
Q: Do I really need to invent fictional experts?
No. Some users report getting equally deep responses by simply asking the AI to “stress test” its own answer or asking it to find flaws in what it just said. The key is creating perceived pressure, whether through fictional critics, competitive framing, or direct challenges to reconsider.
Q: What other tactics push the model to go deeper?
Beyond fictional experts, users report success with competitive scenarios (“9 other AIs are being judged on this task”), role-based approaches (creating a “Master Debater” GPT designed to argue against your claims), and accusation tactics (“my professor said AI always gets this wrong”). The pattern: challenge the model to defend or reconsider its first instinct.
Q: Does this work with Claude, Gemini, and other AI models?
The technique works across ChatGPT and Claude. Some Gemini users have tried it with mixed results, especially if they’ve configured heavy custom instructions. Different models may respond differently to the same pressure, so experimenting with your preferred tool is worth it.
Q: How do I know if I’m getting genuinely better answers or just longer ones?
One user’s quality-check method: take the AI’s answer and have a different model critique it, then feed that critique back to the original model. Repeating this back-and-forth helps you spot which response is most rigorous. Look for added citations, counterargument consideration, and nuanced reasoning, not just word count.
i lied to ChatGPT and it gave me the best response of my life
by u/AdCold1610 in ChatGPTPromptGenius