This is a submission for the Runner H "AI Agent Prompting" Challenge
📚 What I Built
I used Runner H to analyze a research paper directly aligned with my project. I tasked the agent with generating a structured document that extracts and explains the key findings from the paper in a replicable format.
🎥 Demo
⚙️ How I Used Runner H
I started by describing what I wanted Runner H to do using a natural-language prompt:
*“I want you to read this paper comprehensively and identify key steps that they used to achieve their results. After identifying those, give me a detailed explanation of their approach and explain it to me in such a way that I can replicate it. Also give me a summary of everything as well. Create a @tool:Google Docs document. Write into the Google Docs document. The structure should be this way:
- Summary of the Paper
- Phases of the Project
- Key Steps Taken to Come Up with the Solution
- Explanation of the Key Steps They Used (with Code)
- Suggestions on What Next to Do Since I’m Working on a Similar Project”*
Then I asked ChatGPT to refine this prompt for clarity and effectiveness with Runner H. Here's the structured response I received:
🧠 Final Runner H Prompt Structure
1. Summary of the Paper
- A concise summary (150–300 words) capturing the objective, methods, datasets used, key findings, and conclusions.
- Highlight the problem they aimed to solve and the core contribution of the paper.
2. Phases of the Project
- Break down the project into logical phases or stages (e.g., data collection, preprocessing, modeling, evaluation).
- Label and describe each phase clearly.
3. Key Steps Taken to Achieve the Results
- Identify and list the critical decisions, methods, algorithms, or tools used.
- Include any preprocessing techniques, feature engineering, model selection, tuning, evaluation metrics, and tools (e.g., Python libraries, hardware used).
4. Detailed Explanation & Replication Guide (with Code)
For each key step:
- Explain what was done and why.
- Provide code snippets (in Python or pseudocode if needed) that replicate the methodology.
- Note external dependencies or datasets and where to get them.
5. Suggestions for Next Steps in My Own Project
Given that I’m working on a similar project (e.g., predicting bilirubin levels in infants using image data):
- Suggest how to adapt or extend the paper’s methods.
- Mention potential limitations or challenges in the original paper.
- Offer ideas for improvement, modern alternatives, or follow-up experiments.
📝 Additional Notes
- Assume intermediate-level Python and ML knowledge.
- Use diagrams or tables where helpful.
- If parts of the paper are unclear, make educated guesses based on similar research.
💡 Use Case & Impact
This use of Runner H significantly reduces the time and effort typically spent on literature reviews in research. It not only automates the summarization but also creates a reproducible, code-friendly interpretation that can serve as a launchpad for practical experimentation.
❤️ Social Love
If you're a researcher or student working on a machine learning or scientific project, you’ll find this approach incredibly useful!
Check Out this on DEV Community.
— Ben_z (@crypticBen_z) June 24, 2025
Runner H Challenge
Here is what I used the tool for: Automated my Literature Review https://t.co/qjU7qFuTWj
Top comments (0)