An AI-generated report can get the company name right, invent an executive’s quote, and attach a link to a real page that supports neither the quote nor the conclusion.
For professionals using AI to research purchases, summarize documents, or prepare business reports, those mixed answers create a practical problem: which parts can you trust? Checking the writing for obvious mistakes is only a starting point. The important work is tracing each consequential claim to evidence.
A useful review follows three questions: Does the source exist? Does it support the claim? Does the information apply to this decision?
What an AI hallucination looks like
An AI hallucination is false or unsupported information generated and presented as though it were factual. It can appear as a fabricated research paper, an invented quotation, an incorrect product specification, or a summary that adds details missing from the original document.
The National Institute of Standards and Technology’s generative AI risk profile uses the term “confabulation” for confidently presented erroneous or false output. NIST explains that models generate language from learned statistical patterns and can also produce misleading citations or reasoning.
For a reviewer, the distinction between an invented fact, an outdated fact, and a misread source matters less at first than catching the unsupported statement before someone acts on it. Later, identifying the cause can help the team improve its workflow.
Polished wording and professional formatting do not establish that an answer is accurate. AI chatbot warning labels also leave organizations with the practical task of establishing whether users actually verified an answer.
Start by separating the claims
Before opening links, break the answer into statements you can check individually. One sentence may contain several.
Consider this fictional example: “Vendor A’s Enterprise plan costs $20 per user per month, includes unlimited storage, and is available worldwide.”
That requires at least three checks: the price and billing terms, the storage allowance, and geographic availability. Confirming the price does not validate the rest.
Prioritize claims that could change a decision:
- Money: Prices, billing periods, minimum commitments, and calculations.
- Eligibility: Regions, subscription tiers, device models, and account requirements.
- Timing: Launch dates, support deadlines, and whether a feature is available or only announced.
- Authority: Direct quotations, research findings, and statements attributed to named people.
- Operational impact: Configuration instructions, compatibility, permissions, and security guidance.
For a short internal summary, a simple checklist may be enough. For a purchasing recommendation or customer-facing report, record the source and supporting passage alongside each important claim.
Check facts against the right original source
Choose a source that is in a position to establish the specific fact.
For software capabilities, start with current product documentation and release notes. For pricing, check the vendor’s pricing page and relevant terms. For a research finding, locate the paper or report and read its methods and limitations.
An announcement can establish that a vendor introduced a feature. It may leave unanswered whether that feature is available in your region, supported on your devices, or included in your contract.
Check those conditions explicitly. A statement that a tool “supports enterprise accounts” may omit an extra license, administrator setting, or staged rollout.
Also separate what a source establishes from what it claims. A vendor’s benchmark documents its reported result under stated test conditions; it does not establish that your organization will achieve the same performance.
When sources disagree, compare their dates, scope, and underlying evidence. Look for a correction, updated documentation, or a difference in definitions before choosing which statement to use. If the conflict remains unresolved, describe it or leave the claim out.
Verify quotations word for word
Treat every AI-generated direct quote as unverified until you find the original wording.
Virginia Commonwealth University Libraries’ fact-checking guidance recommends checking cited sources directly and tracing quotations to credible records. Searching a distinctive phrase in quotation marks can help locate the original.
Once you find a potential match:
- Confirm the speaker’s identity and role at the time.
- Compare the wording with the original document, transcript, or recording.
- Read the surrounding passage or listen to the surrounding exchange.
- Check whether the statement was conditional, hypothetical, or describing someone else’s position.
- Record a page number or timestamp so another reviewer can reproduce the check.
A real quote can still mislead when separated from its context. “We could expand next year if the pilot succeeds” does not support a statement that expansion is confirmed.
If you cannot verify the exact wording, do not use it as a direct quote. Paraphrase only when a reliable source supports the meaning and attribution; otherwise, omit the statement. An AI tool’s assurance that the quote is accurate does not supply the missing evidence.
Audit citations beyond whether the link opens
A working URL proves that a page is reachable. You still need to establish that it is the intended source and supports the attached claim.
For each consequential citation, check:
| Identity | The title, author, publisher, and document match the citation. |
| Support | A specific passage supports the claim as written. |
| Scope | The population, product, region, and conditions match the claim. |
| Currency | The information remains applicable and has not been superseded. |
| Independence | Corroborating reports provide separate evidence rather than repeating one source. |
For an academic paper, compare the title and authors with the publisher’s record and check the DOI, if one is supplied. A real DOI may point to a different paper.
If a link is broken, search the title on the publisher’s website or in an appropriate catalog. A broken link alone does not prove fabrication. If you cannot inspect the source, mark the claim as unverified.
Do not substitute a search-result snippet or an AI-generated search summary for the source. Read the relevant passage, including nearby footnotes and limitations.
Check numbers and summaries separately
Recalculate important figures with a calculator or spreadsheet, then verify that the inputs match the source.
Check currency, units, time periods, sample sizes, and denominators. An increase from 20% to 25% is five percentage points, or a 25% relative increase. Confusing the two changes the meaning.
For survey findings, establish who responded and what they were asked. A result from a selected group of existing AI users does not automatically describe all workers.
For document summaries, compare the conclusion with the full relevant section. Watch for omitted exceptions, recommendations presented as requirements, and proposed changes described as current policy.
When working with confidential material, use an approved tool and follow your organization’s data-handling rules. Checking accuracy should not require uploading sensitive files to an unapproved service. Controls such as Android Enterprise’s AI management policies address access and automation; teams still need a separate process for reviewing factual output.
Use AI to assist the review
A second AI pass can help identify inconsistencies or organize claims for checking. Agreement between two chatbots should not count as corroborating evidence: both answers may depend on the same mistaken material.
Likewise, a system that searches the web or retrieves company documents still needs review. Open the cited material and compare it with the answer.
Give reviewers a clear way to mark each claim as supported, contradicted, or unverified. For consequential work, retain the original output, the evidence checked, the correction, and the reviewer’s name in an approved location.
Check what your tools actually log. According to Anthropic, Claude for Government’s administrative audit logs record administrative actions, while usage exports contain metering information rather than conversation content. An audit feature may not preserve the evidence needed to reconstruct a factual review.
More must-read AI coverage
- SS&C Intralinks DealCentre AI vs. Datasite: Which platform is built for the future of dealmaking?
- SS&C Intralinks FundCentre AI vs. Juniper Square: Which platform better supports modern private markets fund managers?
- Why Data, Not Models, Determines AI Success
- The Rise of the AI-Native Factory: How Physical AI Is Transforming Manufacturing
Make verification part of the handoff
Before sharing an AI-assisted document, a reviewer should be able to show where its consequential facts came from, locate every direct quote, and explain any remaining uncertainty.
Match the depth of review to what happens next. Brainstorming notes can retain clearly labeled ideas for investigation. A procurement recommendation needs verified costs and eligibility conditions. Instructions that affect production systems need current documentation and technical review before use.
When a central claim remains unsupported, pause the recommendation or narrow it to what the evidence establishes. Labeling a document “AI-generated” does not tell the next reader which statements were checked.
Make that handoff concrete. For each claim that could change a purchase, policy, or technical action, record the source, the supporting passage, the date checked, and any conditions that limit its application. Assign a reviewer who can resolve outstanding questions before the document is used.
Recheck information that can change between drafting and action. Confirm pricing before approving a purchase, availability before planning a rollout, and configuration guidance before changing a production system. A verified draft gives the next person enough evidence to assess the recommendation and identify what still needs checking.
Checking AI’s answers is one step toward using it more effectively. TechRepublic readers get free 7-day access to The Neuron Academy, our practical learning platform designed to help professionals use AI more confidently at work. Browse all lessons →
Let us teach you How to Talk to AI for free! Try our six-minute course at The Neuron Academy and learn a few simple ways to write better prompts and get more useful results from AI, or browse our other AI course for free for seven days. Check out all the lessons here →