The Bottom Line
- Ask for output formats that force grounding: citations, study summaries, and uncertainty.
- Use constraints (population, setting, timeframe) to reduce irrelevant retrieval.
- Always include a ‘limitations’ clause to avoid confident overreach.
The core skill is not ‘prompting’ — it’s structured questioning. You’re trying to force the model into evidence-first behaviour: cite, summarise, and declare uncertainty.
1
Prompt 1 — Evidence table
“Give me a table: Recommendation | Evidence source | Year | Key limitation | Where it applies.”
2
Prompt 2 — Guideline alignment
“Summarise how major guidelines differ on this question and why.”
3
Prompt 3 — What would change the answer
“List the patient factors or setting constraints that would change your recommendation.”
4
Prompt 4 — Counter-evidence search
“Find the strongest counterargument or evidence that would contradict the top conclusion.”
5
Prompt 5 — Practical implementation
“If I had to implement this in a time-pressured setting, what is the minimum safe plan and what are the stop-triggers?”
Practice
Test your knowledge
Apply this concept immediately with a high-yield question block from the iatroX Q-Bank.
SourceOpenEvidence: Product entry point
Open Link SourcePeer-reviewed overview referencing OpenEvidence’s RAG approach (PMC)
Open Link