Picture the last time you asked AI for help with something real: a marketing plan, a tricky email, whatever. It answered fast, sounded confident, and completely missed what you needed. A Redditor going by u/DifferentSecret28 blamed the AI for that exact problem for an entire year before finding the fix. It wasn’t a smarter prompt technique. It was one line added to the end of every request.
The line forces the model to stop and ask questions before it answers anything. Instead of guessing at what you meant from a half-formed request, it interviews you first. It asks one question at a time, until it has enough to actually work with. The original poster says this single addition fixed more of their results than any clever prompting trick they’d tried before.
Why It Actually Works 🧠
AI tools are built to be maximally helpful right now. Ask a question, get an answer, immediately. That default sounds great until you realize what “immediately” usually means. The model fills gaps in your request with its best guess instead of your actual intent.
The interview prompt flips that objective. Instead of resolving your query on thin context, the model has to understand it first. And here’s the part that stings a little: half the time the model’s questions reveal that you hadn’t actually thought the problem through. That’s not a flaw in the AI. That’s information about your own request, not a bug in the tool.
How to Steal This Prompt 🛠️
Add this exact block to the end of any request where you want a genuinely useful answer instead of a generic one:
Before you answer, ask me the questions you need answered to give me a genuinely useful response instead of a generic one. Ask them one at a time. Do not answer until you have what you need. If I have left out a constraint that would change your answer, that is exactly what to ask about.
Here’s the workflow once it’s pasted in:
- Send your original request with the block attached at the end.
- Answer the first question the model asks. Be specific, not vague, since a vague answer just restarts the guessing problem.
- Keep answering one question at a time until the model stops asking and starts responding.
- If it jumps to an answer too early, tell it to keep asking before it responds.
The “one at a time” instruction is doing most of the heavy lifting here. Skip it and you get a wall of ten questions dumped on you at once, and nobody answers a wall of questions well. One commenter on the thread, u/TheUnequalMilton, said the same wall of questions problem shows up in coding prompts. It makes them want to close the tab entirely.
Tips and Tricks 💡
The original poster admitted their version still sometimes jumps to an answer after just two questions. Treat this as a starting point, not a finished tool. A few ways to sharpen it:
- Add a minimum question count, like “ask at least four questions before answering,” if two feels too shallow for your use case.
- Use it for brainstorming and planning tasks first. That’s where vague inputs cause the most damage, since the model has nothing concrete to anchor to.
- Pair it with a hard constraint list. Tell the model upfront what it should never assume, like budget, audience, or tone, so the interview covers gaps instead of basics.
- If the model still rushes to an answer, add “confirm with me that you have enough before responding” as a checkpoint.
- Save your favorite version as a text snippet somewhere you can paste it in two seconds, since you’ll want it on almost every request once it clicks.
Reddit commenter u/Open-Mousse-1665 made a point worth repeating here. The real skill isn’t the prompt itself, it’s knowing how to build the prompt you need in the moment. A single copy-pasted block won’t save a request that’s fundamentally unclear even to you. It just makes that unclarity visible faster, which honestly might be the more useful outcome.
Try It Today 🚀
Next time you’re about to fire off a vague request and hope for the best, paste this block on the end instead. Let the model ask you the annoying questions you were hoping to skip. Answer them honestly, even the ones that feel obvious, since those are usually the ones you’d have skipped explaining. Then head over to the original thread on r/PromptEngineering to see the full back-and-forth and the sharper versions other users are testing.
Frequently Asked Questions
Q: How do you stop the model from answering too early?
Add a hard stop like “do not answer until I say I have nothing more to add” or “confirm with me before you start your actual response.” This forces the model to finish its interview first. The author’s version (asking one question at a time) helps, but some models still jump ahead after 2-3 questions. A confirmation gate prevents that.
Q: Why does asking one question at a time matter so much?
A wall of 10 questions feels like an interrogation and makes people close the tab. Sequential questions keep it conversational and manageable, more like a collaboration than a quiz. You have time to actually think between each answer instead of getting overwhelmed.
Q: Does this work for coding and technical problems?
Absolutely. Force it to ask about architecture decisions first, and you’ll get code much closer to what you actually need. You’ll spend way less time debugging wrong assumptions baked into the solution. It’s especially useful when you’re not 100% sure what you’re building.
Q: Is interviewing the model different from just writing a really detailed prompt?
Yes, the skill isn’t the prompt itself, it’s understanding *how* to prompt. Interviewing is a process that clarifies your own thinking while the model asks. Half the time, the model’s questions reveal you hadn’t actually thought the problem through. That’s valuable feedback about *you*, not a flaw in the LLM.
Q: Can I combine this interviewing approach with collaborative problem-solving?
Yes. Start with “I have this problem, let’s figure it out together” and let the model make assumptions while you correct as you go. By the end, you’ve co-built both a clear model of the problem and some solid directions to explore.
the prompt I paste to make it interview me before it answers, because half my bad answers were bad questions
by u/DifferentSecret28 in PromptEngineering