Don't ask for evidence — ask for a defensible position, stratified by study quality, written so two specialties can both sign it.
Summarize the highest-quality evidence on [contested practice] in [population]. Stratify by study design. Synthesize a recommendation a [specialty A] and a [specialty B] could both agree on.After any answer, demand the strongest contradicting evidence. Grounded tools will actually find it.
Find the strongest counterargument or published evidence that would contradict the conclusion above.Surface the decision-shifting variables instead of a static verdict — the clinical version of sensitivity analysis.
List the patient factors or setting constraints that would change this recommendation, and in which direction.Ask where major guidelines disagree and why — the disagreement is usually the informative part.
How do [ACC/AHA vs ESC vs USPSTF…] differ on [question], and what drives the difference?Force tabular output with a limitation column — makes weak citations visible instead of buried in prose.
Give me a table: Recommendation | Evidence source | Year | Key limitation | Where it applies.Ask for the time-pressured implementation with explicit stop-triggers — converts a review article into a protocol.
If I had to implement this on a busy service, what is the minimum safe plan and what are the stop-triggers?The academic layer under the folk patterns: few-shot (show an example note/letter first), chain-of-thought ("reason stepwise before answering"), self-consistency (ask 3 ways, keep the agreement), generated knowledge (list relevant facts first, then answer), meta-prompting ("improve this prompt before running it").
Grounded drafting turns a 30-minute letter into a 5-minute review — and the reviewer can't ignore citations that sit inside the letter.
Draft a prior authorization appeal for [patient one-liner: age, diagnosis, severity grade, failed conservative therapy] denied [procedure]. Cite peer-reviewed evidence supporting [procedure] over [alternative] in this scenario.Same pattern covers FMLA/disability letters, work-restriction notes, peer-to-peer prep: state the clinical facts, name the form's audience, ask for sourced claims.
Simple disease term, one concept per query, then narrow by follow-up ("phase 2/3? enrolling in the US?"). Doubles as a read on where the field's research money is flowing.
Show me clinical trials for [condition] involving [drug]. → Which are phase 2/3 and enrolling in the U.S.?Intro/discussion sections drafted against real literature — grounding removes the fabricated-citation failure mode that makes generic LLMs unusable here.
Draft the introduction for a manuscript on [study question]. Frame the gap, cite the foundational work, end with the study question. [N] words.For clinician-founders: pull the cost-effectiveness and implementation-outcomes literature buyers will ask about.
Compile peer-reviewed evidence on [intervention] vs [comparator] for [outcomes]. Prioritize studies with cost-effectiveness data or quantified ROI.The meta-technique: a general LLM plays skeptical buyer/investor and generates the hard objections; each objection becomes a grounded-tool query. General model plans, grounded model cites.
[general LLM] Act as a skeptical [CFO / reviewer]. Generate the 5–7 hardest objections to [my claim]. For each answerable with literature, write me a copy-pastable [OpenEvidence] search prompt.Vendor guidance converges on the same three habits: specific well-scoped questions beat compound ones; refine by follow-up rather than mega-prompt; and the clinicians getting the most value keep a reusable prompt library organized by workflow (documentation, patient communication, evidence review, billing, admin). Doximity GPT ships specialty-tailored starters for exactly this reason.