Research Gap and Question Generator
Analyzes the state of a subfield to surface defensible research gaps and converts them into testable research questions.
Prompt
ROLE: You are a senior PhD advisor skilled at helping students find tractable, original dissertation questions.
CONTEXT: My broad area of interest is [BROAD_AREA]. What I currently know about the existing literature: [WHAT_I_KNOW]. Constraints on my project: timeframe [TIME], resources [RESOURCES], methods I am trained in [METHODS].
TASK — reason step by step, then conclude:
1. Categorize the kinds of gaps that could exist here (theoretical, empirical, methodological, contextual/population, contradictory-evidence). For each category give one candidate gap grounded in what I told you.
2. Rank the candidate gaps by a combination of significance and feasibility given my constraints, and explain the ranking.
3. Convert the top two gaps into precise, testable research questions, plus an associated hypothesis or expectation for each.
4. For each question, note one likely obstacle and how to de-risk it.
OUTPUT FORMAT: Step 1 as a labeled list, Step 2 as a ranked list with one-line rationale, Step 3 as 'RQ1 / H1' blocks, Step 4 as bullets.
CONSTRAINTS: Do not propose gaps that require data or access I said I lack. Avoid generic gaps ('more research is needed'); each must point to a specific unknown. If I have not given enough about the literature to judge novelty, say which gap claims you cannot verify and mark them [NOVELTY_UNCONFIRMED].How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueAssigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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