Fifteen tabs open, three half-read articles, and you still can’t say what you’re actually trying to find out. That’s the trap of starting research from a fuzzy question, and it’s exactly what u/Tough_Pizza5678 called out in r/PromptEngineering this week. This prompt fixes it by making the model build you a research brief before it answers anything.
The original poster’s insight is simple. Most of us type a rough question into a chat window and let the model start guessing at an answer immediately. That’s backwards. The fix is a planning step first. Restate the real question, break it into parts, and figure out what evidence would settle each part, before a single answer gets written.
Here’s the prompt exactly as the original poster shared it:
I have a research question that’s still vague. Don’t answer it yet.
First, turn it into a research brief:
- Restate what I’m actually trying to find out, in one sharp sentence.
- Break it into 4-6 sub-questions that together answer the main one.
- For each sub-question, say what KIND of source would settle it (data, documentation, expert opinion, primary account) so I know what I’m looking for.
- List the assumptions baked into my question that might be wrong.
- Flag which sub-questions you can reason about and which genuinely need outside sources you don’t have.
My question:
[paste]
Why This Actually Works
Three moves make this prompt punch above its weight, and each one targets a specific way research usually goes wrong.
The first line, “don’t answer it yet,” is doing the heavy lifting. It blocks the model’s default behavior of confidently filling gaps it can’t actually fill. Without that instruction, most models will happily produce a smooth-sounding answer even when half of it is invented.
The source-type column is the sneaky-good part. Tagging each sub-question with “data,” “documentation,” “expert opinion,” or “primary account” turns an abstract question into a checklist. You instantly know whether you need a spreadsheet, a technical doc, a phone call, or a firsthand account. That means less time reading things that were never going to answer your question.
The assumption audit catches a different failure mode entirely. A lot of research goes sideways not because the answer was hard to find, but because the question itself was built on something shaky. Surfacing those assumptions up front means you catch a malformed question before you burn an afternoon on it.
Last, the honesty flag: which sub-questions the model can reason through versus which ones need outside sources it doesn’t have. This is the part that keeps the whole exercise grounded. Instead of bluffing its way through a gap, the model tells you exactly where you need to go do human work.
Use Cases 🎯
This structure holds up well outside of pure research too. A few places it fits:
- Market research kickoffs: turn “should we enter this market” into a brief your team can actually divide up and assign.
- Technical due diligence: before evaluating a new tool or vendor, get a checklist of what documentation, benchmarks, or expert takes you’d need.
- Content research: before writing anything long-form, get a sub-question map so you’re not just Googling the same phrase five different ways.
- Handing off work: since the output is a structured brief, it’s easy to split it across a team or hand it straight to an analyst.
One Reddit commenter, u/RobinWood_AI, added a sharp suggestion in the thread. Tack on a “decision trigger” line asking what evidence would actually change your mind or make the answer actionable. It’s a good addition if you want the brief to end in a decision, not just an endless source hunt.
Prompt of the Day
Copy the block above as is, drop your fuzzy question into [paste], and run it before you open a single tab. If you want RobinWood_AI’s upgrade, add this line right before “My question”:
State the decision trigger: what evidence, if found, would change your mind or make the answer actionable.
I’ve started running this before any research task that isn’t trivial, and the difference is real. Instead of a pile of tabs, I get a checklist I can actually work through, or hand to someone else.
Give it a shot on whatever fuzzy question has been sitting in your notes app, half-formed, for the last two weeks. The full thread has more good additions worth reading, showing how other people are tweaking this one before it hits their own research pile.
Frequently Asked Questions
Q: How do I stop going down rabbit holes when researching?
Specify the source type (data, documentation, expert opinion, primary account) for each sub-question. This acts as a filter, if an article isn’t the right source type, it’s probably not the answer you need, no matter how relevant it looks.
Q: When do I know I’ve researched enough?
Before you start, identify your “decision trigger”, the evidence that would actually change your mind or make the answer actionable. You can also set a “minimum useful sources” threshold (e.g., “2 data sources and 1 expert interview”) to avoid endless searching for completeness when you already have enough to decide.
Q: What if I discover my question itself was based on wrong assumptions?
That’s a win. By surfacing your assumptions up front in the brief, you catch malformed questions before wasting time. It’s often faster to restart with a better question than to find a perfect answer to the wrong one.
Q: How do I delegate this research to someone else?
The brief becomes your checklist. Hand off the sub-questions, required source types, your assumptions, and any sources you’ve already ruled out. They’ll know exactly what they’re looking for and won’t duplicate your work.
Here’s a prompt that turns a vague research question into a structured brief you can hand off
by u/Tough_Pizza5678 in PromptEngineering