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Verify an AI-assisted report claim by claim before you use it to make a decision or publish it. Open each cited source, check that it is authoritative and current, read the relevant passage in context, and confirm that it supports the report’s exact wording. Then independently check important claims, redo the calculations, record corrections and uncertainties, and have a qualified person review high-impact work. A fluent answer is not evidence, and a citation that exists is not necessarily a citation that supports the claim.
What should I verify first in an AI-assisted report?
Start with claims that could change a decision, cause harm, or damage trust if they are wrong. You do not need to give every sentence the same level of scrutiny: prioritize consequential figures, dates, quotations, rules, scientific or medical statements, named people or organizations, and conclusions that depend on calculations.
Set the stakes before checking claims
Define what the report will be used for, who will rely on it, and what could happen if an important statement is inaccurate. A low-impact internal brainstorm and a report used to guide a health, legal, policy, financial, or operational decision do not call for the same review depth. When the consequences are serious, plan for independent corroboration and subject-matter review rather than relying on a quick citation scan.
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Break prose into atomic claims
Turn each sentence with a checkable assertion into one or more small claims. A sentence might contain a date, a number, a causal explanation, and an interpretation; those are separate things to verify. Record the wording as written, the report location, and whether it is a fact, statistic, quotation, interpretation, or opinion.
For example, a sentence saying that a policy “reduced incidents by 20% last year” contains at least a policy attribution, a causal claim, a percentage, a time period, and an implied comparison. The source may support the incident count but not the causal explanation or the chosen comparison period. Splitting the sentence makes those gaps visible.
How do I verify an AI-generated citation?
Follow the reference to the original publisher or record, then test whether it supports the claim attached to it. A working link, plausible title, or real publication is not enough: AI systems can produce fabricated references and can also attach genuine sources to statements those sources do not establish.
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Check that the reference is real and identifiable
Open each URL, DOI, footnote, or quoted passage instead of trusting the text of the citation. Confirm that the author or issuing organization, title, publication venue, date, and version match the report’s reference. For official or technical claims, trace secondary summaries back to the relevant organization, official database, legislation, regulator, or peer-reviewed record when available.
A reference can fail in more than one way. It may not exist, may resolve to a different work, may identify the right work but omit a relevant version, or may point to a source that is too old for the current claim. Note which problem you find rather than marking every failed check simply “bad citation.”
Read the surrounding evidence, not just the search result
Read the passage that the report relies on and enough of its surrounding text to understand its scope. Check the source’s date, population, method, uncertainty, limitations, and argument. Look for corrections, retractions, replaced rules, or updated documentation that could change what the source means now.
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Then compare the report’s wording with what the source actually says. A page about the same general topic may not support the sentence. Watch for a changed unit, an omitted qualification, a period selected without explanation, a quotation stripped of context, or a descriptive association rewritten as cause and effect. Topic overlap is not claim support.
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Use a support test for every citation
Ask whether a careful reader could locate evidence in the source for the complete claim—not merely for one phrase in it. If only part of a sentence is supported, narrow the sentence or split it. If the source is ambiguous, record that uncertainty instead of treating the interpretation that favors the report as settled fact.
For claims that matter, seek a second credible source that did not simply repeat the AI’s wording. Prefer primary records and recognized authorities, such as official statistics, primary documents, regulators, peer-reviewed research, or established international organizations. Independent corroboration is especially useful when the first source has limits, the claim is contested, or the wording makes a strong conclusion.
How can I tell whether a statistic is being presented fairly?
Check the statistic’s numerator, denominator, unit, population, time period, and method before accepting the headline number. Then compare the figure in the report with the underlying table or dataset and the source’s own explanation of what it measures.
Inspect the statistic’s definition and boundaries
- Population: Who or what is counted? Check whether the report quietly generalizes from a sample, subgroup, or particular place.
- Period: Which dates does the figure cover, and do the report and source use the same interval?
- Denominator: Is the figure a count, a percentage, a rate per unit, or a share of a particular group?
- Method and uncertainty: Does the source explain how the value was collected or estimated, and what limitations apply?
- Comparison: Are both sides of a before-and-after or group comparison measured on a comparable basis?
These checks can reveal misleading presentation even when a number was copied accurately. A correct percentage with the wrong denominator, a rate described as a count, or a result with its population and dates removed can lead readers to a different conclusion than the source supports.
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Look for conclusions that outrun the data. A change over time does not by itself establish why the change happened; an estimate should not be presented as a precise count; and a result for one defined population should not be treated as universal. Preserve qualifiers that materially affect interpretation, including uncertainty, exceptions, and the exact period or group studied.
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There is no single validated AI error-rate figure established by the cited guidance for this workflow. Avoid substituting an invented general accuracy percentage for checking the actual claims in the report.
How do I check AI-generated calculations and tables?
Recompute important results from the underlying data rather than checking only whether the report’s arithmetic looks plausible. Compare your reproduced answer with both the report and the cited source, and investigate any mismatch before using the result.
Get the source data and define the calculation
Locate the table, dataset, or values from which the report says its result was derived. Write down what is being calculated, which rows or groups are included, the units, and the date range. If you cannot obtain the underlying values or the report does not explain the calculation, mark the result as unverified; a citation to a broader report does not make an unseen calculation reproducible.
Reperform the work independently
Recalculate relevant totals, percentages, rates, differences, rankings, and derived values using the source values. No particular software is required by the guidance: choose a method that lets another reviewer reproduce the same operation. For a simple result, that may mean writing out the arithmetic; for a larger table, it may mean using a spreadsheet or another suitable calculation method and retaining the inputs and formulas.
Check more than the final answer. Verify that the right values were transcribed, that the formula matches the question, and that the denominator and units are consistent. Look for missing values, duplicated entries, rounding differences, date-range mismatches, and boundary cases that should be included or excluded.
Reconcile discrepancies instead of choosing the convenient number
If your result differs from the report, trace the discrepancy back through the inputs and method. A mismatch may come from a transcription error, a different denominator, rounding, a changed dataset, or an unstated choice about which observations to include. Record the reason if it can be established; if it cannot, qualify or remove the derived claim rather than presenting either figure as verified.
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Check that a table’s labels, totals, percentages, and ordering agree with its underlying values. A table can contain individually plausible entries but still mislead through inconsistent units, an incorrect total, or a ranking that does not follow from the figures shown.
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How should I document the verification?
Keep a verification matrix that shows what was checked, what evidence was found, what changed, and what remains uncertain. This makes review more useful than a simple “verified” mark and gives another person a path to reproduce important checks.
Use a claim-level record
| Field | What to record |
|---|---|
| Claim and location | The exact report wording and where it appears. |
| Claim type | Fact, statistic, quotation, interpretation, or opinion. |
| Source and evidence | The source identity and the relevant passage, table, or data used to assess the claim. |
| Check performed | For example, a context check, independent corroboration, or calculation reproduced from source values. |
| Result and correction | Whether the wording is supported, what was corrected, or why the claim was rejected. |
| Reviewer and date | Who checked it and when. |
| Uncertainty and status | What could not be established and whether the claim is approved, qualified, or still unresolved. |
Keep the prompt, relevant inputs, tool and model version when known, and the final accepted or rejected output with the record. This helps reviewers understand which material was checked and makes it easier to revisit work if the report or its production context changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should I escalate or repeat the checks?
Get qualified human review for high-impact work, and repeat relevant checks when the information or context behind the report changes. Verification is not a permanent stamp: a source can be superseded, and a revised prompt, tool, model version, or intended use can produce a different report that needs its own review.
Escalate claims that need specialist judgment
Some checks are about more than locating a sentence in a source. Legal interpretation, scientific methods, medical implications, and conclusions that depend on specialized context may require a reviewer with relevant expertise. A general reviewer can flag missing evidence, but should not convert an unresolved specialist question into an approved claim.
Reject unsupported claims or qualify them so the limits are plain. If a material issue cannot be settled with the sources and expertise available, state what remains uncertain or leave the claim out. The House of Commons Library describes AI as an assistant rather than an authority; Government of Canada guidance likewise cautions against treating generated content as authoritative.
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Set clear re-verification triggers
- A source is updated, corrected, retracted, or replaced.
- A rule, policy, dataset, or technical reference changes.
- The prompt, inputs, tool, or model version changes materially.
- The report will be used for a different audience, decision, or level of consequence.
- A reviewer finds a discrepancy that affects related claims, calculations, or conclusions.
When a trigger applies, revisit the claims that depend on the changed material and any downstream calculations or conclusions. Do not assume that a previously checked sentence remains supported after its source or use changes.
Can an AI detector or provenance credential verify a report?
No. Appearance-based AI detection is not proof of authorship or accuracy, so verification should focus on the report’s evidence. Provenance credentials or watermarks may provide supplemental information about origin or edit history, but they do not prove that the report’s claims are true; their absence does not establish authenticity.
Use provenance information, when available, as context about where content came from or how it changed—not as a replacement for opening sources, checking calculations, and reviewing the claim itself. Purdue University Northwest’s CHECK guidance captures the practical principle: important claims from an AI tool should be checked before use.
What should a completed verification look like?
A report is ready for its intended use only when its material claims have a traceable evidence path and its unresolved limits are visible. Before approval, confirm that each important citation supports its attached wording, important data work can be reproduced, corrections are reflected in the final version, and the appropriate human reviewer has signed off.
- Every consequential claim has been separated into checkable statements and assigned a clear status.
- Sources have been opened, identified, read in context, and assessed for currency and scope.
- Important claims have independent corroboration where appropriate.
- Calculations and tables have been recomputed from their underlying values, with units and periods checked.
- Unsupported or overconfident wording has been removed, corrected, or qualified.
- The verification record preserves evidence, decisions, reviewer, date, and residual uncertainty.
- Re-verification is planned if relevant sources, tools, inputs, or use conditions change.
Frequently Asked Questions
Can I ask the same AI to verify its own report?
It can help identify claims, locate possible sources, or suggest arithmetic to inspect, but its answer is not independent corroboration. Treat any suggested evidence as a lead and verify it directly against the source record.
What should I do if a cited source is behind a paywall?
Do not treat the citation as verified merely because its abstract or search snippet appears relevant. Seek an accessible authoritative record or ask someone with legitimate access to inspect the needed passage; otherwise record the limitation and qualify or omit the dependent claim.
Should I keep the AI’s original wording when I correct the report?
Keep the original output in the audit record so the change is traceable, but publish only the corrected wording that the evidence supports. Make sure the correction also updates any chart, summary, or conclusion that relied on the original claim.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsDoes a citation manager or reference list check prove the citations are accurate?
It may help organize references, but a well-formed bibliography does not establish that a source exists in the cited form or supports a particular sentence. The source-to-claim check still has to be made by a reviewer.
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