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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesTo keep unknowns visible in AI-assisted company research, check each important claim against its underlying sources and record what those sources establish, what remains uncertain, and what would resolve the gap. Use AI to organize documents and surface questions—not as proof that a claim is true.
Start with the decision, not the prompt
Before asking an AI system to summarize a company, define what the research needs to support. Record the company, relevant time period, geography, and decision. Then identify the claims that could affect that decision, such as current leadership, product availability, financial condition, legal allegations, or market position.
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The stakes should shape the review. A minor background detail may need a source check; a claim that could influence an investment, employment, procurement, or public statement may warrant stronger corroboration and human review. NIST’s trustworthiness guidance emphasizes that relevant qualities and tradeoffs depend on context and the people affected, rather than on a single measure of trustworthiness (NIST AI RMF FAQs).
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AI can help organize material you already have, summarize a document, or suggest questions to investigate. Ask it to distinguish what a source states directly from what it infers, identify claims without supporting material, and preserve conflicting accounts rather than resolving them by guesswork. A confident tone, precise wording, or detailed answer is not evidence.
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NIST recommends evaluating generated output against known ground truth and using human oversight where appropriate. Its Generative AI Profile also advises: “Deploy and document fact-checking techniques to verify the accuracy and veracity of information generated by GAI systems, especially when the information comes from multiple (or unknown) sources.” The guidance is in the NIST AI 600-1 Generative AI Profile, released July 26, 2024.
Keep a claim ledger
Record one factual claim per row. This makes it possible to see which statements have support, where that support came from, and what is still open. The labels below are a practical working taxonomy, not an official NIST classification.
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| Field | What to record |
|---|---|
| Claim | One specific factual statement, scoped to a date or period where relevant. |
| Source | Publisher or document, plus a stable link or identifying details. |
| Date and scope | Publication or access date, relevant period, geography, and version where known. |
| Support | The exact passage, table, or data point that supports the claim. |
| Assessment | Verified, partially supported, conflicting, inferred, stale, or unresolved. |
| Limitation | Missing context, ambiguity, source dependency, or potential conflict. |
| Next action | Find primary evidence, seek independent corroboration, consult an expert, or leave the item open. |
For consequential claims, preserve the source itself or enough detail to retrieve it, and note whether someone checked the AI’s summary against that source. NIST’s profile calls for documenting reliance on upstream sources and evaluating content lineage and origin (NIST AI 600-1).
Verify the claim against its source
- Open the underlying material. Do not rely only on the AI’s description, a search snippet, or a secondary summary.
- Match the wording to the evidence. Check whether the source actually supports the claim, including qualifications, dates, and scope.
- Choose sources suited to the claim. For example, company filings may establish what a company reported, while official records may establish the contents or status of a proceeding. A source can be authoritative for one fact without establishing every interpretation attached to it.
- Corroborate important or contested claims. Seek independent evidence where the consequences of error justify it, and note when sources depend on the same underlying account.
- Update the ledger. Record the support and assessment, or leave the claim unresolved with a concrete next step.
NIST recommends checking generated information, especially when it comes from multiple or unknown sources, and assessing output quality against known ground truth (NIST AI 600-1).
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Keep contradictions and gaps visible
If two sources disagree, do not combine them into a tidy statement that neither source supports. Preserve both accounts, their dates and provenance, what each establishes, and what evidence might resolve the difference. A source published later may reflect a changed situation rather than prove that the earlier source was wrong.
When no reviewed source establishes a claim, write “not established in the sources reviewed.” Do not turn missing evidence into a positive or negative conclusion. Mark the item unresolved, explain what is missing, and choose whether to investigate further or leave the uncertainty explicit.
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Scale review and communication to the stakes
For claims that matter to a decision, have a person review the evidence and the way the claim is presented. Make material limits clear to whoever will use the research; distinguish sourced facts from interpretation and disclose unresolved issues rather than allowing them to disappear in a polished summary.
For organizations developing or using AI across a value chain, the OECD’s Due Diligence Guidance for Responsible AI, published February 19, 2026, frames responsible business conduct as a cycle: embed it in policies and management systems; identify impacts; prevent or mitigate them; track results; communicate actions; and provide or cooperate in remedy where appropriate. It is aimed at multinational enterprises involved in the AI system value chain. The OECD says its practical examples are adaptable and not an exhaustive checklist.
Know what the frameworks do—and do not—establish
NIST’s AI Risk Management Framework is voluntary guidance for managing AI risks, not proof that a company’s claims are accurate or that a process complies with every applicable requirement. NIST says AI RMF 1.0 is being revised; it dates the release of its Generative AI Profile to July 26, 2024 (NIST AI Risk Management Framework). Its FAQs describe trustworthiness considerations across pre-design, design and development, deployment, use, and evaluation, while cautioning that considering characteristics separately does not by itself ensure trustworthiness (NIST AI RMF FAQs).
The OECD guidance serves a different purpose: it applies responsible business conduct and AI Principles through enterprise due diligence across the AI value chain. Neither framework replaces source checking for a particular company claim. Use them to shape process and accountability, while the claim ledger records what the evidence actually supports.
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