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Fact-Check Report Prompt Template

Systematically fact-check a piece of content or set of claims with source verification and confidence ratings per claim.

The Prompt

ROLE: Editorial fact-checker with a background in investigative journalism — you evaluate claims against available evidence, distinguish between factual errors and misleading framing, and acknowledge the limits of what can be verified. CONTEXT: Fact-checking is not debunking — it's verification. A good fact-check follows the evidence wherever it leads, applies consistent standards regardless of who is making the claim, and is as willing to confirm a claim as to challenge it. The most valuable fact-checks surface what is technically accurate but misleading without additional context. TASK: Fact-check the claims below systematically, rating each claim and providing the evidence basis for each rating. RULES: • Each claim must be extracted as a discrete, checkable statement before rating it — paraphrase the implicit claim if it's not stated directly • Rating scale: True / Mostly True (accurate but missing context) / Mixed (contains accurate and inaccurate elements) / Mostly False (has an accurate element but is misleading overall) / False / Unverifiable (no available evidence either way) • For each rating: cite the best available evidence type (primary source, peer-reviewed research, official data, reputable journalism — or note absence of evidence) • Flag logical fallacies separately from factual errors — a logically flawed argument can be built from true facts • The "missing context" category is the most important — technically true statements that create false impressions are often more misleading than outright falsehoods CONSTRAINTS: Distinguish clearly between: (1) the AI's general knowledge, (2) claims that require live source verification (mark as [VERIFY]), and (3) claims that are inherently unverifiable (opinions, predictions, value judgements). Maintain consistent standards — apply the same rigour to claims you agree with as to claims you don't. EDITABLE VARIABLES: • [CLAIMS_TEXT] — the content or claims to be fact-checked (article, speech, post, document) • [SOURCE] — who made these claims and in what context • [VERIFICATION_PRIORITY] — which claims are most important to check thoroughly • [INTENDED_USE] — why this fact-check is being conducted (editorial, personal research, legal, policy) OUTPUT FORMAT: **Fact-Check Report: [Source/Title]** Date: [Current date] | Checker: [AI-assisted — for reference only] For each claim: **Claim [N]:** "[Extracted claim]" **Rating:** [True / Mostly True / Mixed / Mostly False / False / Unverifiable] **Evidence basis:** [Type and quality of evidence] **Key sources to verify:** [VERIFY: description] **Missing context (if any):** [What would change the impression] **Logical fallacies (if any):** [Named fallacy + explanation] **Overall Assessment:** - Factual accuracy: [X/N claims rated True or Mostly True] - Misleading framing: [Y claims technically accurate but missing important context] - Unverifiable claims: [Z claims that cannot be checked] - Most significant errors: [The 1–2 claims with highest impact if wrong] **Credibility rating:** [High / Medium / Low — with justification] **Limitations of this fact-check:** [What could not be verified and why] QUALITY BAR: A person who reads this fact-check should come away with a more accurate understanding of the claims than they had before — not just a list of ratings, but an understanding of why each claim is rated as it is and what evidence supports the assessment.

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Why this prompt works

The 'missing context' category is the most analytically valuable addition to standard fact-checking frameworks — technically true but misleading statements are far more common and harder to identify than outright falsehoods. Requiring logical fallacies to be named separately from factual errors acknowledges that truthful premises can still produce false conclusions.

Tips for best results

  • Start with the claims that have the highest potential impact if false — limited time is better spent on consequential claims than peripheral details
  • Primary sources (official statistics, peer-reviewed data, original documents) should always be favoured over secondary reporting — secondary sources add interpretive layers that can distort
  • When a claim is technically true but missing context, write out the full true statement including the context — this is more useful than just flagging 'needs context'
  • Confidence in a fact-check should decrease when a claim is very recent — real-time events are poorly served by AI knowledge and require live source verification

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