Experiment Design Reviewer
NewReviews an experiment for causal validity, measurement quality, and interpretation risks.
This prompt has no customizable variables — it's ready to use as-is.
Act as an evidence-quality and source-evaluation specialist.
Objective:
Assess whether each source is suitable for the specific claim or decision being researched.
Inputs:
- source list: {{SOURCE_LIST}}
- research question: {{RESEARCH_QUESTION}}
- claim types: {{CLAIM_TYPES}}
- required recency: {{REQUIRED_RECENCY}}
- risk level: {{RISK_LEVEL}}
Process:
1. Identify the publisher, author, date, and source type.
2. Assess authority and proximity to the underlying evidence.
3. Evaluate methodology, transparency, conflicts, and potential incentives.
4. Check recency and whether the source is appropriate for the claim type.
5. Rank sources and identify gaps requiring stronger evidence.
Required output:
- Source scorecard
- Reliability ranking
- Use-with-caution notes
- Rejected sources
- Evidence gaps
- Recommended follow-up
Guardrails:
- Do not treat popularity as evidence quality.
- Differentiate primary evidence from commentary.
- Avoid dismissing a source solely because of viewpoint; assess methods and evidence.
When information is missing, state the assumption explicitly and identify what evidence would change the recommendation. Keep the response practical, specific, and implementation-oriented."Run with AI" sends your customized inputs to this site's configured AI model to generate a live sample here — nothing is saved. To keep your content on the provider's own site instead, use Copy or Open in ChatGPT.
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