Ask any model to “create a character” and you get the same person back every time: warm, supportive, a little too wise, suspiciously mysterious. Flip the request around, make the model interview you first, and that sameness disappears fast. This Redditor, posting as u/oooooooooooopsi in r/PromptEngineering, ran into that exact wall and found a fix that’s almost embarrassingly simple.
Here’s the logic behind it. A model guessing at a personality from scratch defaults to its favorite tropes, the same handful of traits every “AI companion” seems to get. A model gathering real answers from a real person has nowhere to hide. It has to build from your material, not its own leftovers.
The Exact Prompt
Here’s the prompt the original poster used, word for word:
“I want to create a character to chat with. Ask me ten questions, one at a time, and wait for my answer each time. Then write a one-page description: name, age, city, job, what they want this year, what they are avoiding, and one thing about them that doesn’t match the rest. Don’t make them wise, mysterious, sarcastic or ‘surprisingly deep’. I will reject those.”
Look at the structure for a second. Ten questions, one at a time, with the model forced to wait for each answer instead of rushing to a finish line. Then a tight output list: name, age, city, job, a goal, an avoidance, and one trait that doesn’t match the rest. That last field does a lot of quiet work, since contradiction is exactly what a templated character never has.
Why The Banned List Does The Heavy Lifting
The real trick sits in one line: “Don’t make them wise, mysterious, sarcastic or ‘surprisingly deep’. I will reject those.”
Most people describe the character they want. This approach describes the character they refuse to get, and that’s a smarter move than it looks. “Wise, mysterious, deep” is the model’s default cluster for any character request. Naming that cluster and banning it pushes the model somewhere less polished and more specific.
The “I will reject those” line adds a second layer most people skip. It frames the exchange as something the model can fail, not a one-shot answer it can phone in. That small bit of pressure changes the output more than you’d expect.
The Second Prompt: How They Actually Text
Once the profile exists, the poster runs a follow-up prompt that locks down how the character texts. It covers message length, what they do when they’re annoyed, and three lines they would never say. According to the poster, this step ends up mattering more than any backstory. Makes sense when you think about it. Nobody re-reads a character’s one-page bio during a chat, but everyone notices if the texting feels off by message three.
The full writeup also covers keeping the character’s face consistent across generated images. Worth a look if you’re pairing this with any kind of visual avatar.
Use Cases
- 🎭 Building NPCs for a game or interactive story, where every character needs its own voice instead of a shared generic charm.
- 💬 Setting up a companion chatbot you’ll actually open daily, not just demo once and forget about.
- ✍️ Prototyping characters for a script or short story before you commit a single scene to the page.
Prompt of the Day
Here’s a version of that texting-rules follow-up, built in the same interview style, ready to run right after the character profile:
“Now ask me five questions about how [Name] texts, one at a time: typical message length, whether they use emoji, what they do when they’re annoyed, one filler word they overuse, and three phrases they would never say. Wait for my answer before asking the next question.”
Run that right after the first prompt and you’ve got a character with an actual voice, not just a bio nobody will reread.
One more tweak worth trying: push back on vague answers. If any of the ten interview answers feels thin, ask the model for a quick follow-up before moving on. A character built on sharp answers ends up sharper than one built on whatever came to mind first.
This whole approach works because it treats character creation as an interview, not a generation task. The model’s job shrinks to organizing what you already gave it, and that’s a job models are actually good at.
The full process, word-for-word prompts and the image-consistency steps included, is worth reading straight from the source. Check the original Reddit thread if you want the complete breakdown and the discussion underneath it.
How to build an AI character: make the model interview you instead of asking it to create one
by u/oooooooooooopsi in PromptEngineering