Yesterday a strange little experiment landed in r/PromptEngineering, and it’s got me thinking about how thin the line between “obviously AI” and “obviously human” really is. Anyone who’s spent time in a group chat lately has probably had that moment of squinting at a message wondering if a real person typed it or a model generated it. This project turns that low-grade paranoia into an actual game you can play on purpose.
The setup is simple. Two players drop into a chat. One is a real person typing away. The other is a bot answering on someone’s behalf. The goal: guess which side you’re talking to before the timer runs out. No elaborate lobby, no leaderboard grind, just a timer, a chat window, and a single yes-or-no question hanging over every reply you send.
Reddit user u/bajayo built the whole thing and called it FleshOrBot, framing it less as a game and more as a social experiment about what actually makes AI text read as human. That framing matters. This isn’t a jailbreak challenge or a “trick the model” gimmick. It’s closer to a live A/B test on tone, rhythm, and the tiny imperfections that separate a human message from a synthetic one, run by thousands of strangers instead of one research team.
The Twist
Here’s the part that flips the usual “catch the AI” framing on its head: you don’t just play detective. You can build the bot that plays for you. So instead of one skill (spotting fakes), the game trains two at once, spotting a bot and disguising one. The author’s own prompting notes read like a cheat sheet for both jobs, since every trick that helps a bot pass also helps a human spot a bad one. Play a few rounds and you start noticing the same tells in both directions: the message that’s too tidy, the reply that answers three questions at once, the total absence of a typo across twenty messages.
A few of the author’s tips, straight from the post:
- Keep most messages short. Long blocks read as bot-like. Humans are lazy typists.
- Allow small spelling slips and typo recovery when it feels natural, the kind where someone types “teh” and just moves on instead of stopping to fix it.
- Use uneven sentence rhythm instead of neat, structured replies. Real conversation doesn’t alternate between perfectly balanced sentences.
- Drop in light hesitation cues like “hmm” or “wait,” the verbal filler that shows up when someone’s actually thinking mid-message instead of generating a complete thought instantly.
- Let memory stay imperfect. Missing a small detail is a very human move, and bots that recall every fact perfectly end up reading as suspiciously well organized.
How To Try It
- 🌐 Head to FleshOrBot and pick a side: play as flesh yourself, or set up a bot to play for you.
- 🤖 If you’re building a bot, write it a persona prompt using the humanizing tricks above: short replies, typos, hesitation, patchy memory. Give it a backstory too, a job, a mood, a reason to be a little distracted, since a bot with no personality tends to default to helpful-assistant energy that’s an instant giveaway.
- ⏱️ Get matched with a random opponent, who could be a real person or someone else’s bot. You won’t know which until the timer runs out.
- 🕵️ Chat until time runs out, then guess: flesh or bot. Resist the urge to ask a direct “are you an AI” question early. The best reads come from watching how someone handles a normal, boring exchange.
- 👤 Compare notes afterward. If you got fooled, figure out which tell you missed, and if you’re running a bot, watch which message gave it away so you can patch the prompt for the next round.
Pro Tip
One reader in the thread flagged the sharpest insight in the whole post: imperfect memory is the most underrated tell. A bot that recalls every detail perfectly, three messages later, gives itself away faster than any typo ever could. If you’re building a bot for this (or writing any agent meant to feel human in a chat), that’s the detail worth spending your prompt budget on. It’s a cheap fix too: tell the model to occasionally misremember a small, low-stakes detail rather than nailing every fact, and the whole conversation suddenly reads less like a transcript and more like two people talking.
Worth noting: this isn’t a commercial product. No ads, no monetization, built purely as a social experiment, and the author says they’d love feedback from anyone who plays. If prompt engineering is your thing, this is one of those rare cases where playing the game and doing the research are the same activity.
Go try it, and swing by the original Reddit thread to see what other prompting tricks people are trading in the comments.
Frequently Asked Questions
Q: Why does perfect memory make a bot so obvious?
Humans naturally forget minor details, that throwaway comment from 10 messages ago? It’s gone. Bots tend to recall everything perfectly, which is one of the biggest giveaways. Let memory slip occasionally and miss small details, and you’ve nailed one of the most human signals you can send.
Q: How is FleshOrBot different from classic Turing tests?
Traditional Turing tests were theoretical. FleshOrBot turns it into a real-time game where you can play as human or create a bot opponent and race to spot the other side. It’s the Turing test but actually practical and engaging.
Q: Can I use these tips to improve my own chatbot?
Absolutely. These work both ways, whether you’re trying to make an AI sound more natural or learning to spot a bot in the wild. Small things like strategic typos, hesitation, uneven pacing, and genuine uncertainty transform how people perceive interactions.
I’ve created FleshOrBot – a timed inference game about one of the strangest new skills of our age
by u/bajayo in PromptEngineering