The pace of biomedical discovery has never been faster. With mountains of omics data, a flood of new publications, and the pressure to move from hypothesis to breakthrough, researchers can feel like they’re navigating a maze with no map. Tools like K-Navigator, developed by Owkin, promise to be the compass for the modern scientist, pointing the way to the next big insight.
But even the smartest compass won’t get you far unless you know how to chart your path. In the world of AI-powered research, your questions—your prompts—are everything. The difference between a vague query and a targeted, well-structured one could be the difference between a generic answer and a genuinely novel lead.
5 Essential Tips for Effective Prompting
1. Be Specific About Context
The more detail you provide on your research question or disease area, the better the AI can target its analysis.
Example: Instead of “Tell me about cancer genes,” try “Identify key mutated genes in high-grade serous ovarian cancer.”
2. Specify Data Types and Sources
If you know which datasets or data modalities are relevant, mention them directly.
Example: “Analyze single-cell RNA sequencing data from the MOSAIC Window dataset for glioblastoma.”
3. Use Scientific Language
Framing your question in research terms helps the AI understand your intent and deliver more relevant answers.
Example: “Summarize mechanisms of resistance to EGFR inhibitors in non-small cell lung cancer.”
4. Request Evidence and Citations
Ask the AI to back up its findings with recent literature or database references.
Example: “Provide recent studies (last 2 years) on immunotherapy response rates in melanoma, with PubMed IDs.”
5. Define Your Output Format
If you want results in a specific format—table, summary, list—say so.
Example: “List the top 5 differentially expressed genes in bladder cancer as a table, including pathway associations.”
6. Bonus tip: Choose the Right AI Research Assistant
K-Navigator by Owkin is purpose-built by scientists for scientists—unlike generalist chatbots, it’s designed for rigorous biomedical research. For insights you can trust, choose an AI that speaks your language!
Good vs. “Almost Good” Prompts: What’s the Difference?
Not all prompts are equal—sometimes small changes can unlock much deeper insights. Here are some real-life inspired examples:
Prompt to improve:
“Analyze the TCGA dataset for breast cancer and tell me about important genes.”
- How to Improve: What does “important” mean—most mutated, most prognostic? Which data type (genomic, transcriptomic)? How should results be presented?
- Better Prompt:
“Using TCGA breast cancer data, identify the 10 most frequently mutated genes and summarize their roles in disease progression. Present your findings in a table with citations.”
Prompt to improve:
“Compare ovarian and bladder cancer using the MOSAIC Window dataset.”
- How to Improve: What aspect should be compared—gene expression, clinical outcomes? Which data modality?
- Better Prompt:
“Compare transcriptomic profiles of ovarian and bladder cancers in the MOSAIC Window dataset. Highlight genes with shared upregulation and discuss their potential as biomarkers.”
Prompt to improve:
“What are the latest treatments for lung cancer?”
- How to Improve: Specify the subtype (e.g., EGFR-mutated), timeframe, and ask for supporting data or studies.
- Better Prompt:
“Summarize targeted therapies approved for EGFR-mutant non-small cell lung cancer since 2022, with links to supporting clinical studies.”
Final Thoughts
AI tools like K-Navigator by Owkin are transforming biomedical research, not by replacing scientists, but by amplifying their ability to generate and test hypotheses, integrate data, and keep pace with the literature. Getting the most out of these tools starts with clear, thoughtful communication.
The next time you engage your AI assistant, remember: a well-crafted prompt isn’t just a question—it’s the key to unlocking the answers that move your research forward.
