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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAssume factual AI output is unverified until you check it. Break the answer into individual claims, confirm that every citation exists, test whether it actually supports the claim, and corroborate important points with current, independent evidence. Increase the depth of review when an error could affect money, health, safety, legal rights, grades or professional decisions.
What it means to verify an AI answer
Verification is not the same as checking whether an answer sounds plausible or contains a neatly formatted reference list. It is a claim-by-claim process that asks four separate questions:
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- Is the factual claim specific enough to check?
- Does the cited source actually exist?
- Does that source support the precise statement attributed to it?
- Is the claim current, relevant and appropriate for the decision you are making?
An answer can fail at any stage. A paper may exist but say something different from what the model claims. A real law or statistic may have changed. A quotation may be invented even when the author and publication are real. Fluent language and confident wording are presentation qualities, not evidence.
The House of Commons Library’s May 19, 2026 briefing presents AI as an assistant rather than an authority and places responsibility for checking and editing the final work on the user. University library guidance from Penn State, the University of Alberta and the University of New England gives compatible advice: trace citations, assess source quality, seek independent confirmation and look for missing context.
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A practical verification workflow
Use the following sequence for any answer that matters. For a casual, low-consequence question you may stop after a quick check; for a consequential decision, complete every step and involve a qualified reviewer.
1. Split the response into individual claims
Copy the answer into a working document and mark every statement that could be true or false. Treat these as separate claims:
- Dates, numbers, percentages and rankings.
- Statements about what a study, law, policy or organization says.
- Named people, institutions, products, events and locations.
- Quotations and paraphrases.
- Cause-and-effect statements, predictions and comparisons.
- Links, footnotes and bibliographic details.
Do not verify a paragraph as one unit. A paragraph can contain a correct background fact, an unsupported inference and a fabricated citation. Give each claim an identifier, such as C1, C2 and C3, so you can record the result without losing track.
2. Record exactly what the answer asserts
Write the claim in a neutral, testable form before searching. Preserve the qualifier that changes its meaning: the country, edition, population, time period, unit, confidence level or condition. “The policy bans this practice” is different from “the policy recommends avoiding this practice in one setting.”
| Field | What to record |
|---|---|
| Claim ID | A short label such as C1 |
| Exact claim | The smallest factual statement you can check |
| Claim type | Statistic, quotation, legal rule, historical fact, interpretation, prediction or other |
| Scope | Place, date, population, edition, unit and exceptions |
| AI citation | Author, title, publisher, date, link or identifier as presented |
| Verification result | Supported, partly supported, contradicted, unverifiable or not applicable |
| Evidence note | The passage, data table or official record that determines the result |
3. Establish that each citation exists
Search the title, author and publication details in a library catalogue, scholarly database, official repository or a reputable search engine. Try distinctive phrases from the title rather than relying on the model’s link. For a report, check the issuing organization’s publication list. For a paper, check the journal, DOI record or institutional repository. For a law, regulation or court decision, search the official legislative or court source.
Check spelling, publication year, journal volume, page range and author order. Small inconsistencies can reveal that a real source has been blended with invented details. If you cannot locate the work after reasonable searches, mark the citation unverified; do not treat it as evidence and do not repeat it as a source.
4. Read the source instead of trusting the AI’s description
Open the original record and inspect the abstract, methods, results, limitations and conclusion as appropriate. A citation’s existence says nothing about whether it supports the answer. Models may attach a real paper to a conclusion that the paper never tested, confuse a review with an original experiment or mistake a source’s discussion of a possibility for a finding.
Search within the source for the key term, number or quoted phrase. For a statistic, locate the table or official series that produced it. For a quotation, confirm the exact wording, speaker, date and context. For a legal statement, read the relevant provision and any definitions, exceptions or effective dates.
5. Test claim-to-source fit
Compare the scope of the claim with the scope of the evidence. Ask:
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- Does the source address the same population, place and period?
- Does it measure the same outcome or define the term in the same way?
- Is the AI presenting correlation as causation?
- Has a conditional or tentative finding been rewritten as a universal rule?
- Does the cited passage support the entire sentence or only one clause?
- Are important limitations, exclusions or uncertainty intervals omitted?
Record a claim as partly supported when the source supports only a narrower version. Rewrite the statement to match the evidence rather than stretching the source to preserve the AI’s wording.
6. Confirm important claims independently
Penn State advises checking factual claims against at least two reputable sources. “Independent” means more than two pages repeating the same press release or AI-generated summary. Prefer a primary record when one is suitable, then use an authoritative secondary source to explain it.
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- For a scientific result, use the original paper and, when useful, an independent review or replication.
- For a legal requirement, use the current primary text and authoritative guidance for the relevant jurisdiction.
- For a company announcement, distinguish the company’s own statement from independent reporting or regulatory filings.
- For a historical event, compare a reliable archival record with a scholarly account that explains its evidence.
Two sources are not automatically better than one if both copy the same unverified assertion. Trace each one far enough to identify the underlying evidence.
7. Check currency and context
Dates, statistics, policies, prices, software behavior and institutional leadership can change. Find the publication date, last-updated date and effective date, then ask whether they match the question. A current page may describe an older event; an old page may no longer describe a current rule.
Check the source’s purpose and perspective. A marketing page, advocacy statement, preliminary result and official regulation can all be useful, but they answer different questions and have different incentives. Keep the source’s qualifications attached to the claim when you take notes or quote it.
8. Look for omissions and framing
Read the answer for what it leaves out. Does it present a disputed interpretation as settled? Are relevant populations, counterexamples or risks missing? Does a precise number create a false impression of certainty? Does a confident verb—“proves,” “guarantees” or “always”—claim more than the evidence?
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The University of Alberta’s guidance recommends asking whether fact is distinguished from opinion or speculation and whether the response supplies enough context to interpret the evidence. A useful correction may require adding a limitation, not merely replacing one number.
9. Escalate review when the consequences are high
The House of Commons Library recommends expert review and clear responsibility for edited final work. Use a qualified person when a mistake could materially affect a decision, especially in health, law, finance, safety, employment, education or public policy. An expert can identify a missing qualification or a source that is inappropriate for the discipline even when the citation looks authentic.
How to evaluate the quality of a source
Use the five-part CRAAP lens described by the University of New England Library Services: currency, relevance, authority, accuracy and purpose. The framework is a set of questions, not a score that makes a source automatically trustworthy.
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| Criterion | Questions to ask | Warning signs |
|---|---|---|
| Currency | When was it published or updated? Is the information still valid for the date in question? | No date, obsolete policy, old statistics presented as current |
| Relevance | Does it answer this exact question, for this population and decision? | A general article used to support a narrow technical or local claim |
| Authority | Who produced it? What expertise, institution or accountability do they have? | Anonymous authorship, unclear affiliation or credentials unrelated to the claim |
| Accuracy | What evidence and methods are supplied? Can the claim be checked independently? | Unsupported assertions, broken references, unexplained numbers or internal contradictions |
| Purpose | Is the source informing, selling, advocating, entertaining or persuading? | Undisclosed commercial interest, selective evidence or emotional framing |
Apply the same questions to a source the AI recommends and to a source you find yourself. Authority is contextual: an official statistics office is authoritative for its own series, while a specialist researcher may be better placed to interpret a technical result. No universal source hierarchy fits every discipline.
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Use SIFT to investigate online claims
The University of New England describes a SIFT-style approach for online evaluation. It complements, rather than replaces, close reading of the original evidence.
- Stop. Pause before sharing or accepting a striking claim. Identify exactly what you need to know and whether the wording is trying to provoke urgency or certainty.
- Investigate the source. Find out who produced the page, what organization stands behind it, what expertise it has and whether its purpose could shape the presentation.
- Find better coverage. Look for reporting, reference material or analysis from sources with stronger methods, clearer accountability or closer access to the underlying evidence.
- Trace to the original. Follow a quotation, statistic, image or claim back to the first available document. Check whether later pages changed its wording or dropped its qualifications.
Tracing matters because search results often surface summaries that quote one another. The earliest page you find is not necessarily the original; confirm authorship and publication history where possible.
Special checks for statistics, quotations and lists
Statistics and numerical claims
Capture the definition, unit, denominator, time period, geography and source series. Ask whether the number is a count, rate, percentage, estimate, forecast or one-time allowance. Check the methodology and whether revisions or sampling limits apply. Do not add a dramatic prevalence or “hallucination rate” simply because it would make an article persuasive. The available guidance does not establish one named statistic that is necessary for this verification procedure.
When an AI gives a figure without a primary publisher, year and original table or series, mark it as unverified until those details are found. Recalculate percentages when practical; transcription and denominator errors are common ways a plausible number becomes misleading.
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Search a distinctive phrase in quotation marks, then open the original speech, interview, book, transcript or document. Confirm the speaker, date, audience and surrounding passage. A polished sentence that fits a person’s views is not proof that the person said it. If you find only an institutional summary, paraphrase it without quotation marks and attribute the guidance to the institution.
Named studies and references
Check that the title, author list, venue, year and identifier all point to the same work. Distinguish a preprint from a peer-reviewed publication, a commentary from original research and a review from a new experiment. Read the abstract and methods far enough to determine what was actually studied.
Lists, rankings and comparisons
Identify the ranking publisher, date, criteria and edition. “Best,” “largest” and “most effective” depend on a defined metric. Verify that every item was eligible under the same rules and that the answer has not mixed different years or categories.
How to handle disagreement between sources
Do not choose the source with the most confident prose. Compare the underlying evidence and ask whether the sources are answering the same question.
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- Align the date, geography, population, definitions and outcome.
- Identify which source is closer to the original evidence.
- Compare methods, sample selection, measurement and stated limitations.
- Check whether one source is reporting a preliminary result while another includes later evidence.
- Look for independent corroboration and perspectives omitted by each account.
- Explain the disagreement in the final answer instead of silently selecting a winner when uncertainty remains.
The Alberta framework emphasizes support, reliability, framing, perspectives and the consequences of relying on a claim. A disagreement may reflect different definitions or time windows rather than a simple factual contradiction.
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| Risk level | Examples | Minimum sensible review |
|---|---|---|
| Low | Casual background fact with no decision attached | Check the key claim and citation; note uncertainty if the source cannot be found |
| Moderate | School assignment, workplace briefing or public-facing explanation | Verify every factual claim, inspect cited passages and use independent reputable confirmation for important points |
| High | Health, legal, financial, safety, employment or policy decision | Use current primary or authoritative sources, document the evidence, check alternatives and obtain qualified expert review |
| Critical | Immediate action affecting life, liberty, major assets or public safety | Do not rely on the AI answer as the decision authority; consult the responsible professional or official service directly |
This scale prevents both careless acceptance and wasteful checking. A low-risk question may need a quick source trace, while a high-risk claim deserves a documented audit trail and human accountability.
Common failure modes and how to fix them
“The citation looks real, so it must be real”
Why it fails: AI can combine plausible author names, titles, journals and dates into a reference that does not exist.
Fix: Locate the record in an authoritative catalogue or repository and compare every bibliographic detail.
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“The source exists, so it supports the answer”
Why it fails: A real source may address a different population, outcome or conclusion.
Fix: Read the relevant passage, table or legal provision and rewrite the claim to its supported scope.
“Two websites repeat the same sentence”
Why it fails: Repetition may be copying, not independent corroboration.
Fix: Trace both pages to the original record and seek a genuinely independent source.
“The newest search result is current”
Why it fails: Search ranking is not an update guarantee, and a current page may describe an earlier period.
Fix: Check publication, update and effective dates against the question’s time frame.
“A precise answer is a reliable answer”
Why it fails: Precision can conceal an unsupported estimate, an omitted denominator or false certainty.
Fix: Find the method and uncertainty, or state that the figure could not be verified.
“A content detector proves the sentence is false”
Why it fails: Provenance and authorship signals are different from factual verification. NIST’s November 20, 2024 report discusses provenance, labeling and detection approaches for synthetic content; those approaches do not establish whether a particular sentence is factually true.
Fix: Verify the claim and source directly. Use provenance information as context, not as a substitute for evidence.
Prompts that make checking easier
You can ask an AI system to present material in a form that is easier to audit, but its output remains unverified. Useful requests include:
- “Separate this answer into atomic factual claims and number them.”
- “For each claim, list the exact source, publication date, relevant passage and any stated limitation.”
- “Label each statement as fact, interpretation, prediction or opinion.”
- “Identify which claims are time-sensitive and what date each one describes.”
- “State what evidence would contradict each claim.”
- “Do not invent a citation; mark the source as unavailable if you cannot identify one.”
These prompts improve the audit trail, not the truthfulness of the generated answer. Still verify every citation and claim yourself.
A reusable verification worksheet
For a report, article or assignment, keep a record like this for each material assertion:
- Claim: Copy the exact sentence or write a neutral version.
- Scope: Add date, place, population, unit, definition and exception.
- Source check: Record where the cited work was found and whether its bibliographic details match.
- Passage check: Note the page, table, section or provision that bears on the claim.
- Independent check: Add a second reputable source when the claim matters.
- Assessment: Mark supported, partly supported, contradicted or unverifiable.
- Revision: Narrow, qualify, replace or remove the claim.
- Reviewer: Record who approved high-stakes content and when.
Keep the original AI wording separate from your edited version. That makes it possible to see which claims were changed, which sources were rejected and where uncertainty remains.
What ChatGPT and other AI systems can and cannot establish
OpenAI’s Help Center documentation, “Does ChatGPT tell the truth?”, warns that ChatGPT can produce incorrect information and should not be treated as an authority. The same practical caution applies to other generative systems: an answer may be useful for brainstorming, summarizing or locating leads, but the system’s confidence, formatting or apparent explanation does not validate the underlying facts.
AI output can also omit relevant context or present an incomplete account. Ask for uncertainty and source details, then perform the external checks described above. When the answer will be published, submitted or used to make a consequential decision, the human editor remains responsible for the final wording and evidence.
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Quick Recap
Final publication checklist
- Every factual statement has been separated into a checkable claim.
- Each important citation has been located in an authoritative catalogue, repository or official record.
- The source supports the exact claim, not merely a related topic.
- Important facts have independent corroboration appropriate to the subject.
- Dates, definitions, units, jurisdictions and editions match the question.
- Quotes have been checked against the original wording and context.
- Statistics include their publisher, year, scope and method where relevant.
- Missing perspectives, limitations and uncertainty are visible.
- Claims are labeled as fact, interpretation, prediction or opinion where that distinction matters.
- The review depth matches the consequences of being wrong.
- A qualified person has reviewed high-stakes material.
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