prompts to elicit innovative ideas - chunhualiao/public-docs GitHub Wiki
Here’s a curated set of prompts designed to elicit innovative ideas from large language models (LLMs) for research proposals, along with explanations of their effectiveness:
1. Challenging Assumptions
Prompt: "What untested assumptions underlie current approaches to [topic], and how could overturning them lead to groundbreaking research?"
Why Effective: This forces the LLM to identify foundational beliefs in the field, exposing gaps or biases. By questioning the status quo, researchers can reframe problems and explore radical hypotheses.
2. Interdisciplinary Fusion
Prompt: "How could methodologies from [discipline X, e.g., neuroscience, game theory, or synthetic biology] be adapted to solve unresolved challenges in [topic]?"
Why Effective: Cross-disciplinary thinking sparks combinatorial creativity. LLMs trained on diverse datasets can synthesize unconventional connections, leading to hybrid solutions.
3. Speculative Scenario Building
Prompt: "Imagine a world where [key constraint in the field] no longer exists. What new research questions or technologies would emerge in [topic]?"
Why Effective: Removing constraints encourages "blue-sky" thinking. The LLM leverages its generative capacity to envision transformative futures, helping researchers reverse-engineer actionable steps.
4. Analogy-Driven Innovation
Prompt: "What systems in nature or society mirror [topic's core problem], and how do they solve it? Can these analogies inspire novel methodologies?"
Why Effective: Analogies promote pattern recognition. LLMs excel at drawing parallels between disparate domains, offering biomimetic or sociotechnical insights.
5. Contradiction Resolution
Prompt: "Identify conflicting theories or data in [topic]. What experiments could resolve these contradictions and redefine the field?"
Why Effective: Highlighting contradictions pinpoints knowledge fractures. LLMs can propose integrative experiments, turning ambiguities into research opportunities.
6. Emerging Tech Impact
Prompt: "How might breakthroughs in [AI/quantum computing/etc.] disrupt traditional approaches to [topic] within the next decade?"
Why Effective: LLMs extrapolate trends from vast data, forecasting how cutting-edge tools could revolutionize methodologies or open new inquiry avenues.
7. Extreme Parameter Exploration
Prompt: "What if [critical variable in topic] were scaled by 100x? What uncharted phenomena or risks might this reveal?"
Why Effective: Pushing variables to extremes tests theoretical boundaries, helping researchers identify latent behaviors or scalability challenges.
8. Ethical Foresight
Prompt: "What ethical dilemmas could arise from solving [topic's problem], and how might addressing them proactively shape innovative frameworks?"
Why Effective: Ethics-driven prompts ensure societal relevance. LLMs anticipate unintended consequences, fostering responsible innovation that strengthens proposals.
9. Reverse Engineering Utopia
Prompt: "Define the ideal outcome for [topic]. What intermediate breakthroughs are needed to achieve it, and which are most feasible?"
Why Effective: Working backward from a vision helps structure milestones. LLMs chunk grand challenges into tractable steps, balancing ambition with practicality.
10. Methodological Cross-Pollination
Prompt: "Which underutilized techniques from [field Y] could be repurposed to collect or analyze data in [topic], and why?"
Why Effective: LLMs act as methodological databases, suggesting overlooked tools (e.g., agent-based modeling for ecology) that enhance rigor or efficiency.
Why These Work
- Cognitive Diversity: Prompts push LLMs to simulate lateral thinking, counterfactual reasoning, and systems analysis—skills that mirror human creativity.
- Gap-Driven: Many prompts target knowledge gaps, contradictions, or constraints, aligning with funding agencies' prioritization of transformative research.
- Future-Oriented: By emphasizing trends, ethics, and speculative scenarios, proposals gain forward-looking appeal.
- Structured Freedom: The prompts balance open-ended exploration with focused goals, guiding LLMs to produce actionable yet novel ideas.
By iterating through these prompts, researchers can harness LLMs as brainstorming partners to uncover high-impact, interdisciplinary, and ethically grounded research directions.