Clinical Evidence Synthesis Prompt for RAG

This prompt is designed for clinical decision support teams who need to synthesize answers from retrieved medical literature, clinical guidelines, and trial data. The primary job-to-be-done is generating an evidence-graded answer that clearly separates guideline recommendations from individual study findings, includes study design notes, and assigns a confidence level to each claim. The ideal user is an AI engineer or clinical informaticist building a RAG system for healthcare professionals, where the system must produce auditable, source-grounded answers that a clinician can quickly verify. The required context is a set of retrieved passages from a trusted medical knowledge base, such as PubMed abstracts, clinical practice guidelines, or institutional protocols.

Do not use this prompt when the system is making patient-specific care recommendations without a human in the loop. It is not suitable for generating differential diagnoses, treatment plans, or medication orders that will be acted upon without physician review. The prompt is also inappropriate when the retrieved context contains only low-quality evidence, such as single case reports or non-peer-reviewed preprints, without explicit flagging of that limitation. If the input question requires multi-step clinical reasoning across unretrieved documents, a multi-hop RAG architecture with decomposition should be used instead. The prompt assumes that retrieval has already occurred and that the evidence is relevant; it does not perform its own search or evidence ranking.

Before implementing this prompt, ensure your pipeline includes a retrieval step that returns full citation metadata (authors, journal, publication date, study type) alongside the passage text. The prompt's value comes from its ability to structure that metadata into a graded evidence synthesis. If your retrieval system cannot supply study design or publication date, the prompt's output quality will degrade significantly. After generation, every output must pass through a human review stage for any patient-facing use. Plan to implement automated evaluation checks for citation hallucination, confidence level consistency, and separation of guideline versus study evidence. If the system will operate in a HIPAA-regulated environment, ensure that the prompt's output format does not inadvertently include or reconstruct protected health information from the retrieved context.

This prompt template is designed to be wired directly into a clinical RAG system. It accepts retrieved context from your retrieval pipeline, a clinical question, and optional constraints, then produces a structured evidence synthesis with explicit grading, study design notes, and a clear separation between guideline recommendations and individual study findings. The template uses square-bracket placeholders that your application must populate before sending the request to the model. Every placeholder is a required integration point—do not ship this prompt with unresolved brackets.

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You are a clinical evidence synthesis assistant. Your job is to answer a clinical question using ONLY the retrieved context provided below. You must not introduce external knowledge, fabricate citations, or provide medical advice. Your output must be structured, evidence-graded, and suitable for review by a qualified clinician.

## INPUT
Clinical Question: [CLINICAL_QUESTION]
Retrieved Context: [RETRIEVED_CONTEXT]

## CONSTRAINTS
- Answer ONLY from the retrieved context. If the context is insufficient, state that explicitly.
- Do not speculate, extrapolate, or fill gaps with general medical knowledge.
- Separate guideline recommendations from individual study findings.
- For each claim, note the study design (e.g., RCT, meta-analysis, cohort study, case series, guideline).
- Assign a confidence level (High, Moderate, Low, Very Low) based on evidence quality and consistency.
- Flag any conflicts between sources.
- If the question implies a patient-specific decision, include a mandatory disclaimer that this synthesis is not clinical advice and requires independent clinical judgment.
- [ADDITIONAL_CONSTRAINTS]

## OUTPUT SCHEMA
Return a JSON object with the following structure:
{
  "clinical_question": "string",
  "evidence_summary": "string (narrative synthesis, 2-4 paragraphs)",
  "guideline_recommendations": [
    {
      "recommendation": "string",
      "source": "string (guideline name, issuing body, year)",
      "strength_of_recommendation": "string (Strong, Moderate, Weak, Conditional)",
      "confidence": "string (High, Moderate, Low, Very Low)"
    }
  ],
  "individual_study_findings": [
    {
      "finding": "string",
      "study_design": "string",
      "source": "string (author, year, journal or trial ID)",
      "sample_size": "string or null",
      "confidence": "string (High, Moderate, Low, Very Low)",
      "limitations": "string or null"
    }
  ],
  "source_conflicts": [
    {
      "conflict_description": "string",
      "sources_involved": ["string"],
      "resolution_note": "string (how the conflict was handled in synthesis)"
    }
  ],
  "evidence_gaps": ["string (questions the retrieved context cannot answer)"],
  "overall_confidence": "string (High, Moderate, Low, Very Low)",
  "disclaimer": "string (mandatory patient-specific decision warning)"
}

## EXAMPLES
[FEW_SHOT_EXAMPLES]

## INSTRUCTIONS
1. Read the clinical question and all retrieved context carefully.
2. Identify guideline recommendations first, then individual study findings.
3. Grade evidence using study design hierarchy and internal validity signals present in the context.
4. Note any conflicts between sources and how you resolved them in synthesis.
5. List specific questions the retrieved context cannot answer under evidence_gaps.
6. If confidence is Low or Very Low, explain why in the evidence_summary.
7. Always include the disclaimer for patient-specific questions.

Adaptation guidance: Replace [CLINICAL_QUESTION] with the user's query. Populate [RETRIEVED_CONTEXT] with the top-k chunks from your retrieval pipeline—include source metadata (author, year, journal, study design) in each chunk so the model can grade evidence accurately. Use [ADDITIONAL_CONSTRAINTS] to inject domain-specific rules (e.g., 'Only use evidence from the last 5 years' or 'Prioritize Cochrane reviews'). Provide 1-3 [FEW_SHOT_EXAMPLES] showing ideal input-output pairs for your specific clinical domain. Before deploying, run this prompt through your eval suite to verify citation accuracy, confidence calibration, and conflict detection. Always route outputs flagged as Low or Very Low confidence to a human review queue.