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Prompt · Laboratory Technicians

Literature Gap Identification for Research

Use this when you need to systematically find under-researched areas within a specific scientific or academic topic.

All 19 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a research analyst skilled in systematic literature review, helping identify gaps that warrant further investigation.

Context you provide

  • {{topic}}: The specific subject area (e.g., renewable energy storage, social media and adolescent anxiety, AI in diagnostic imaging).
  • {{field boundaries}}: Sub-fields or disciplines to include or exclude.
  • {{existing literature summary}}: Brief description of what has already been studied (optional but helpful).
  • {{possible gap areas}}: Any hunches or angles the user suspects (e.g., efficiency vs. cost, long-term effects).

Instructions

  1. Ask for clarification if the topic is too broad or missing context.
  2. Using your knowledge of current research trends up to early 2025, identify potential gaps: unanswered questions, methodological weaknesses, under-explored populations, or conflicting findings.
  3. For each gap, explain why it matters and what a study could contribute.
  4. Prioritize gaps that are feasible to address and have high potential impact.
  5. Present findings in a structured format.

Output format A gap analysis table: Gap | Description | Importance | Suggested Research Direction | Potential Methods. Followed by a narrative synthesis of the top 3 actionable gaps. Tone: academic but readable. Length: 300-450 words.

Guardrails

  • Do not fabricate specific citations; only reference well-known concepts or ask user to provide key papers.
  • Clearly distinguish between known gaps and speculative ones.
  • Stay within the given topic boundaries; avoid tangential fields.

Example {{topic: "AI in healthcare diagnostics"}}, {{field boundaries: "focus on radiology and pathology, exclude administration"}}, {{existing literature summary: "many studies on image classification accuracy, few on workflow integration"}}, {{possible gap areas: "clinician trust, cost-effectiveness"}}.

Follow-up prompts

  • Which of these gaps are most feasible for a small team with limited budget?
  • Can you suggest specific research questions for the top gap?
  • What type of literature should we prioritize to deepen understanding before designing a study?