Difficulty: 🟡 Intermediate | Time: 1-2 hours to understand patterns | Prerequisites: Familiarity with AI chat interfaces
These are starting points, not scripts. Your research questions, disciplinary norms, and analytical goals will shape different prompts. Learn the structure and principles here, then adapt them to your specific needs. The goal is developing your own prompting intuition, not copying templates.
Part A: Example Prompts (Start Here)
These three examples show what structured prompts can look like. Study the patterns, notice what makes them work, then experiment with building your own.1. Theory Synthesis Prompt
Why this works: Clear structure, specific tasks, asks for integration.2. Grey Literature Integration
Why this works: Acknowledges different source types, asks for critical evaluation.3. Thematic Analysis for Qualitative Studies
Why this works: Method-aware, looks for patterns and differences.Part B: The IMO Framework
Understanding the IMO Structure
The International Mathematical Olympiad (IMO) paper referenced in the Case Study shows how AI can think systematically:- Hypothesis: Form an initial understanding.
- Verification: Test the hypothesis against evidence.
- Refinement: Improve based on what you found.
- Iteration: Repeat until you reach a solid synthesis.
IMO Template for Systematic Reviews
This template shows one way to reinterpret the IMO framework for research. You’ll likely adapt it significantly based on your specific research questions and field.Developing a SYSTEM prompt
A system prompt is a powerful tool, but it’s most useful when a specific persona or set of constraints is needed across a multi-step conversation. It sets the “rules of engagement” for the AI. For a one-off task, a detailed user prompt (what you would send to the AI in the chat) is often more effective.
Part C: Prompt Structure Template
The ROLE/CONTEXT/TASK/FORMAT/CONSTRAINTS Framework
Many effective prompts for complex research tasks use a structure like this. We’ve found this pattern helpful, though you might discover other structures that work better for your needs:Applied Example
When to use this structure: We find this helpful for complex, multi-step analysis where AI benefits from clear guidance. For simpler tasks, a direct question often works better. Experiment to see what fits your workflow.
Part D: Your Prompt Workspace
Use these templates as inspiration to develop and refine your own cognitive blueprints for research tasks. Adapt them freely to match your research context.Template: Initial Synthesis Prompt
Template: Refined Prompt (After Feedback)
Template: Agentic Workflow Design
For multi-agent workflows, decompose your research task into specialized roles:Part D: Organizing Your Work
Creating Your Prompt Folder
- Create a personal folder in your workspace for organizing prompts (e.g.,
my-research/prompts/
) - Version your prompts: Save as
synthesis-v1.md
,synthesis-v2.md
, etc. - Keep your favorites: Build a collection for future research
- Export to Cherry Studio: Copy your best prompts to your AI workspace
Prompt Development Tips
- Start simple: Basic structure first, then add complexity as needed
- Test iteratively: Try your prompt on 2-3 papers, then refine based on what you learn
- Document what doesn’t work: Note failures and why—they’re valuable data
- Version your experiments: Keep track of what you’ve tried
- Share with peers: Learn from each other’s approaches and adaptations
Why This Approach Works
Rather than memorizing templates, this approach helps you build prompts that:- Match your specific research needs and questions
- Integrate methodological concepts you’re learning
- Evolve through experimentation and feedback
- Scale to agentic systems when needed
Enhance Your Practice
Related Resources: