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Audit the evidence status of claims

Autor: AILesson8 min de configuraçãoTestado com:ChatGPTRevisado em: 2026-08-28

Resposta rápida

Map each atomic claim to evidence, contradiction, source quality, and a calibrated status. Forneça: Claims to audit, Evidence corpus, Evidence standard and use. Resultado esperado: A claim-evidence matrix, unsupported-claim list, and corrected claim set.

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Audit the evidence status of every claim against the supplied corpus.

Claims or document:
[claims]

Sources with IDs, dates, authors, methods, and passages:
[evidence]

Intended use, status labels, quality rules, currentness, and burden of proof:
[standard]

Split compound sentences into atomic claims and preserve their exact scope, modality, comparison, time, and attribution. For each claim return type, supporting sources, contradicting sources, directness, source quality and independence, recency, population/measure fit, limitations, and calibrated status using only the supplied label rules. Distinguish no evidence supplied from evidence of absence. Do not count repeated reports of one underlying source as independent corroboration, infer causality from association, or upgrade an abstract, headline, opinion, or vendor claim beyond its evidence. Flag circular citations and source conflicts. Finish with corrected wording for overstated claims, missing evidence priorities, and claims that should be removed for the intended use.
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Da entrada ao resultado

Um exemplo prático

Veja como um contexto concreto transforma esta receita em um resultado útil

Entrada real

Claims to audit
C1: The four-day-week trial increased productivity by 20% and improved wellbeing for all employees. C2: It caused no customer-service decline. C3: Most organizations can adopt it without additional staffing.
Evidence corpus
S1 (2025 company report): one 80-person software company compared self-reported completed tasks during a 12-week voluntary pilot with the prior quarter; reported +20%; seasonal workload changed; no control group. Wellbeing survey: 61/80 responded, 49 reported improvement, 8 no change, 4 worse. S2 (2025 company support dashboard): median response 3.9h before and 4.1h during; target ≤4h; complaint count not supplied. S3 and S4 are news articles that both summarize S1 without new data. S5 (2024 cross-sector survey abstract): 38 organizations reported varied staffing changes; full methods unavailable; abstract does not report adoption feasibility.
Evidence standard and use
For a public guidance article. Labels: Supported, Partly supported, Unsupported, Contradicted, Unclear. Causal language needs a credible comparison; 'all/most' needs population evidence. Company reports may support observations about that pilot but not generalization. Evidence current through 31 December 2025.

Exemplo de saída

C1a “trial increased productivity” | causal | S1 reports +20% self-reported completed tasks | no independent source; prior-quarter comparison, seasonal change, voluntary pilot, no control | Partly supported only as an observation, not causal. Corrected: “The company reported 20% more self-reported completed tasks during its 12-week pilot than in the prior quarter; the design cannot establish causation.” S3/S4 are not independent. C1b “improved wellbeing for all employees” | universal | S1: 49 of 61 respondents improved, 8 unchanged, 4 worse; 19 non-respondents | Contradicted. Corrected: “49 of 61 survey respondents reported improvement.” C2a “no customer-service decline” | universal/absence | S2 shows median response changed 3.9→4.1h and exceeded ≤4h target; other service measures absent | Contradicted for the target claim and unsupported as a universal statement. Corrected: “Median response time rose from 3.9 to 4.1 hours; other service effects were not reported.” C3 “most organizations can adopt without staffing” | generalization | S5 mentions varied staffing changes but no feasibility result | Unsupported. Remove from public guidance until full multi-organization staffing evidence is available.

Priorities: obtain S1 definitions/raw task distribution, comparable control/seasonality analysis, complete service metrics, and S5 full methods/results.

Por que isso funciona

  1. 1

    Atomic claims prevent mixed-status sentences from receiving one misleading evidence label.

  2. 2

    Source independence and fit checks prevent citation count from substituting for evidential strength.

Verifique o resultado

  • Was every compound statement split without changing scope or certainty?

  • Are support, contradiction, absence, and missing evidence distinguished?

  • Does each status account for source quality, independence, recency, and population fit?

Use com confiança

Perguntas frequentes

Respostas práticas sobre quando usar esta receita, o que fornecer e onde a revisão humana ainda é importante

What should I prepare before using “Audit the evidence status of claims”?

For “Audit the evidence status of claims,” prepare Claims to audit, Evidence corpus, and Evidence standard and use. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Audit the evidence status of claims” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A claim-evidence matrix, unsupported-claim list, and corrected claim set—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Audit the evidence status of claims”?

The published test record for “Audit the evidence status of claims” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

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