AI Output Evidence Auditor

The Problem

AI-generated reports can look highly professional while containing unsupported claims, hidden assumptions, inconsistent numbers, weak citations, or conclusions that go beyond the available evidence.

Finding these problems manually is slow. Simply asking another AI to “check the report” can also produce another confident but shallow review.

What It Does

AI Output Evidence Auditor evaluates AI-generated professional work through a structured evidence and quality-control process.

It:

Extracts material factual, numerical, and decision-relevant claims. Maps claims to the evidence and sources supplied with the output. Separates supported facts, partial evidence, inferences, assumptions, unsupported claims, and contradictions. Audits source quality, source independence, dates, and citation-to-claim alignment. Reconciles checkable calculations, percentages, totals, ratios, margins, and repeated metrics. Detects contradictions between executive summaries, tables, calculations, and detailed sections. Identifies potential hallucination and unsupported-claim indicators without making unsupported accusations. Evaluates whether recommendations are actually connected to the evidence. Highlights high-impact claims that require additional verification. Produces a structured Claim & Evidence Register. Identifies missing evidence and defines what should be verified next. Produces a professional Reliability Conclusion based on the available evidence. Why This Beats Prompting It Yourself

A generic prompt such as “check this report for errors” can easily produce another subjective AI review.

This skill uses a dedicated evidence-audit methodology that separates:

Evidence from assertion Fact from inference Assumption from conclusion Missing evidence from contradictory evidence Evidence confidence from business impact Source quality from source availability Reconciled numbers from unverifiable calculations Recommendations from the evidence supporting them

Instead of simply rewriting an AI-generated report, it audits the reasoning, evidence, calculations, and decision logic behind the output.

Core Workflow

AI Output → Claim Extraction → Evidence Mapping → Source Audit → Numerical Reconciliation → Contradiction Detection → Assumption Analysis → Recommendation Traceability → Verification Plan → Reliability Conclusion

Audit Modes

Full Audit Complete evidence, source, numerical, contradiction, assumption, and recommendation review.

Evidence Audit Focused review of claims, supporting evidence, evidence gaps, and unsupported assertions.

Numerical Audit Focused reconciliation of calculations, percentages, totals, ratios, periods, and repeated metrics.

Source Audit Focused review of citations, source quality, source independence, dates, and claim-to-source alignment.

Decision Audit Focused review of conclusions and recommendations and whether they are adequately supported by the underlying evidence.

Use Cases Market research quality control Financial report review SaaS metrics validation Procurement and supplier analysis Real estate analysis review Business strategy audit Executive report quality control AI research verification Investment-analysis quality checks Pre-presentation review of AI-generated work Internal quality control for AI-assisted analysis Built For Founders Analysts Consultants Finance teams Procurement teams Operations teams Researchers Product and strategy teams Professionals using AI to produce business reports Evidence Discipline

The skill does not treat a claim as verified simply because it sounds plausible, contains a precise number, or is written with confidence.

Each material claim is evaluated against the evidence available to the audit.

Possible evidence statuses include:

Supported Partially Supported Inference Assumption Unsupported Unverifiable Contradicted

Evidence confidence and business impact are evaluated separately to avoid creating false certainty.

Known Limitations

The skill audits the material provided to it. It does not automatically establish that an external claim is true merely because no contradiction was found.

Independent verification may require primary sources, official records, original datasets, or other authoritative evidence.

When external verification is required, the skill identifies what should be checked and what additional evidence is needed.

Important Note

This tool provides analytical and informational quality-control support. It is not a substitute for professional legal, financial, accounting, regulatory, or other specialist review where such review is required.