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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe reliable way to stop an AI agent from smuggling unsupported claims into a research summary is to audit every factual statement individually. Build the summary from relevant source passages, split the draft into atomic claims, attach evidence to each claim, check that the evidence supports the claim’s full wording and scope, and mark unresolved points instead of guessing. This reduces risk; it cannot guarantee zero errors.
Fluent prose and confident wording are not proof. AI systems can produce incorrect or misleading statements, including fabricated citations, studies and quotations. OpenAI’s guidance recommends verifying important information against reliable sources and treating confidence as separate from accuracy.
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What an unsupported claim looks like
A claim is unsupported when the cited material does not actually establish the proposition as written. The source may mention the same topic yet fail to support the claim’s subject, number, date, population, geography, causal wording or level of certainty.
For example, a source saying that a pilot study observed an association in 120 participants cannot by itself support “the treatment works for everyone.” The summary has enlarged the population, changed association into causation and removed the study’s limited setting. A citation beside the sentence does not repair those changes.
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OpenAI warns that language models may invent references, quotations and studies, and that a model’s confidence is not a reliability measure. Read the warning in the Help Center before allowing an agent to produce publishable research copy.
The claim-by-claim workflow
Use the following sequence for every research summary, whether an agent is answering one question or producing a long report.
1. Retrieve relevant, authoritative material
Start with documents that answer the actual question, not merely documents that contain matching keywords. Keep the source passages, headings and publication details available for review. A retrieval system should score document chunks for relevance before synthesis; NIST describes this kind of pipeline in its agentic-AI evaluation work.
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- Prefer primary studies, official statistics, standards, regulatory material and first-party documentation when they are available.
- Record the source date, edition, geography, population and conditions alongside the passage.
- Keep contrary or limiting passages instead of retrieving only evidence that agrees with the first answer.
2. Split the draft into atomic factual claims
Rewrite each sentence so that it makes one checkable assertion. A sentence containing a number, comparison and causal explanation may need three rows. Separate reported facts from the agent’s interpretation, recommendation or calculation.
OpenAI’s factuality evaluation describes claim extraction as its first stage: relevant factual claims are listed before they are checked against evidence. The same discipline makes a human review practical.
3. Attach a passage to every claim
For each row, record the document and the exact passage that is supposed to support it. Do not accept a citation simply because it concerns the same subject. Check whether the passage supports the whole proposition and the strength of its wording.
4. Check scope and context
Compare the claim with the source’s population, time period, geography, edition, sample, measurement method and stated limitations. Preserve qualifiers such as “in this sample,” “may,” “associated with” and “as of June 2025.” A summary that removes those boundaries is making a new, broader claim.
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5. Verify quotations and numbers against the original
Open the source before publishing any quotation, statistic, date, percentage or table value. Check spelling, units, denominator, rounding, comparison group and whether the number is a one-time result or a recurring figure. OpenAI specifically cautions that fabricated quotes, studies, citations and references can occur; verification must be against the original material, not against another AI response.
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6. Label the result
Keep four outcomes distinct:
- Supported: the passage entails the claim at the stated scope and certainty.
- Contradicted: the source says something incompatible with the draft.
- Insufficient: the source is relevant but does not establish the complete proposition.
- Unresolved: available evidence is ambiguous, missing or conflicting.
For insufficient or unresolved rows, qualify the language, report the disagreement, or omit the claim. OpenAI’s factuality evaluations allow an “unsure” result and permit abstention when uncertainty is too high; an agent should have the same escape route.
7. Recheck the finished summary
Compare the published wording with the claim ledger. Confirm that every material factual statement has a supporting passage and that editing has not made the wording stronger than the evidence. NIST’s probes are intended to return a rationale showing how evidence supports—or fails to support—a claim.
A practical claim ledger
A spreadsheet, database table or structured JSON object is enough. Use one row per atomic claim:
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| Field | What to record |
|---|---|
| Claim text | One checkable factual assertion in the exact wording planned for publication. |
| Source and passage | Document title, URL, section or page, and the passage that bears the claim. |
| Source date and scope | Publication date, population, geography, edition, time window and test conditions. |
| Support status | Supported, contradicted, insufficient or unresolved. |
| Caveat needed | Any limitation, uncertainty, denominator, comparison group or scope boundary that must remain visible. |
| Final wording | The publishable sentence after the evidence check, or “omit” when it cannot be defended. |
This ledger is an editorial implementation of the dimensions NIST describes; it is not a guarantee that an agent will be factual.
How to test whether a citation really supports a claim
NIST’s described probes provide three useful tests: faithfulness, completeness and sufficiency. Apply all three rather than checking only whether a link opens.
Faithfulness: does the passage support the words?
Ask whether the source entails the proposition as written. Watch for changed modality (“may” becoming “does”), changed relationship (“associated with” becoming “causes”), reversed comparison, or a number copied without its denominator.
Completeness: did the summary preserve the source’s message?
Check whether important qualifications, exceptions, uncertainty and contrary findings survived. A sentence can be literally compatible with a passage yet misleading because it leaves out the passage’s central limitation.
Sufficiency: is this source adequate for this claim?
A blog post may be sufficient for a product’s stated feature but not for a medical-effect estimate. A single small experiment may illustrate a possibility but not establish a universal performance guarantee. Judge the source’s authority, method and coverage relative to the proposition.
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These tests distinguish a genuine evidence relationship from topical relevance. Retrieval and citations help a reviewer find material; they do not prove that the material supports the claim.
Keep uncertainty visible instead of guessing
Ambiguous wording, unavailable data and conflicting documents limit what a summary can responsibly say. When the evidence does not settle a question, the safest output is an explicit qualification or abstention, not a smooth completion.
- Use “the available source reports” when the statement is limited to one document.
- Use “evidence is insufficient to determine” when a relevant source does not answer the question.
- Use “estimates vary” and name the differing conditions when sources disagree.
- State what is unknown rather than inventing a precise value.
OpenAI’s discussion of hallucination and safety evaluations emphasizes that accuracy remains below 100% on challenging tasks and that abstaining can be preferable when uncertainty is high. See Why language models hallucinate and the joint safety-evaluation findings.
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Most overclaims are scope failures. Add the boundary next to the number or result, not in a distant footnote.
- Time: “in the 2024 survey” is not “currently.”
- Geography: a result from one country or region does not automatically describe another.
- Population: findings in trained professionals, adults or a particular sample should not be generalized to everyone.
- Edition and version: software, policies and specifications can change; identify the version tested.
- Conditions: preserve laboratory setup, prompts, datasets, duration and comparison baseline when reporting performance.
When a source gives a confidence interval, sample size or stated limitation, retain it if it changes how a reasonable reader would interpret the result.
How to report model-performance figures responsibly
Performance percentages belong to an evaluation, not to every future use of a model. OpenAI’s GPT-5 System Card reports a 26% smaller hallucination rate for GPT-5-main than GPT-4o and a 65% smaller hallucination rate for GPT-5-thinking than OpenAI o3 under the system card’s stated evaluations. Those are comparative results for the named systems, metric and test setting; they are not a general probability that a GPT-5 summary is correct.
| Reported figure | What must accompany it | What not to claim |
|---|---|---|
| 26% smaller hallucination rate | GPT-5-main compared with GPT-4o, using the System Card’s evaluation conditions and metric. | “GPT-5 hallucinates 26% less in every task.” |
| 65% smaller hallucination rate | GPT-5-thinking compared with OpenAI o3, using the same qualified context. | “GPT-5-thinking is 65% more accurate for any research summary.” |
The GPT-5 System Card also discusses claim-level and response-level factuality measures. Do not reproduce chart values unless you have checked the chart’s population, denominator, metric definition and test conditions.
Compare verification approaches by what they actually check
When choosing an agent pipeline or review tool, compare capabilities rather than marketing labels.
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| Approach | Evidence granularity | Support judgment | Uncertainty handling | Auditability and scope |
|---|---|---|---|---|
| Whole-document review | Document-level. | Often establishes topical relevance, not whether each sentence is entailed. | May leave the agent to infer confidence. | Fast to scan, but difficult to trace individual wording and qualifiers. |
| Passage-level retrieval | Relevant chunks or passages. | Improves inspection; still requires a support check. | Can expose conflicting or missing passages. | Better traceability when section and date are stored. |
| Claim-level ledger | One atomic assertion at a time. | Explicitly tests faithfulness, completeness and sufficiency. | Can mark unsupported or unresolved claims and abstain. | Strongest audit trail when the exact passage and rationale are retained. |
The table describes operational trade-offs synthesized from NIST’s evaluation dimensions and OpenAI’s claim-level and uncertainty guidance; no approach guarantees a zero-error summary.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prompt and pipeline controls that make the workflow enforceable
Require evidence before prose
Have the agent return a structured record containing the claim, source passage, scope, support status and caveat before it drafts paragraphs. Reject a row with no passage instead of allowing the model to fill the gap from memory.
Separate fact, interpretation and advice
Use different fields or labels for reported findings, the agent’s synthesis and recommendations. A reviewer can then challenge an interpretation without mistaking it for a quoted result.
Make abstention an accepted output
Define explicit responses such as “insufficient evidence,” “conflicting sources” and “not stated.” If the only allowed output is a fluent answer, the system is pressured to guess.
Run a final claim-to-source diff
On the last pass, compare every sentence containing a number, superlative, causal verb, quotation or named entity with its stored passage. Flag edits that remove qualifiers or broaden scope.
Common failure modes and repairs
“The citation is present, so the claim is verified.”
Why it fails: the source is merely about the same topic. Repair: quote or capture the exact passage and test faithfulness, completeness and sufficiency.
“The agent sounded uncertain, so it must be safe.”
Why it fails: hedging language does not establish evidence, while confident language does not establish truth. Repair: assign a support status from the source, not from tone.
“A benchmark percentage is a universal accuracy rate.”
Why it fails: the metric depends on models, prompts, dataset, evaluator and definition. Repair: report the named comparison and conditions, then state what the result does not establish.
“A summary can omit the inconvenient limitation.”
Why it fails: omission changes the source’s message and can turn a narrow observation into a broad conclusion. Repair: preserve material caveats next to the claim.
“Missing information should be completed from general knowledge.”
Why it fails: the completion may be fabricated or out of date. Repair: mark the claim unresolved, retrieve an authoritative source, or omit it.
A publication-ready review checklist
- Every material factual sentence is split into atomic claims.
- Each claim has an identifiable source passage, not just a subject-matching link.
- The passage supports the claim’s wording, certainty, number and comparison.
- Source date, geography, population, edition and conditions match the claim.
- Quotes, references, figures, units and denominators were checked against the original.
- Important limitations and contrary evidence remain visible.
- Unsupported, contradicted and unresolved claims are labeled, qualified or removed.
- Benchmark results name the compared systems, metric and evaluation setting.
- The final edited prose still matches the ledger after headings, transitions and shortening.
What this method can—and cannot—do
Claim-level checking makes unsupported wording easier to detect, gives reviewers a traceable rationale and creates a deliberate place to abstain. It does not make retrieval complete, sources authoritative or models infallible. NIST presents its agentic-AI probes as a described evaluation approach under development, not a finalized universal standard. Use the workflow as risk control and editorial discipline, not as proof that a summary contains no hallucinations.
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Frequently Asked Questions
How do I stop AI from making things up?
Require a source passage for every atomic factual claim, check the passage against the exact wording and let the agent mark unresolved claims instead of guessing.
How can I check whether AI citations support its claims?
Test faithfulness, completeness and sufficiency: confirm that the passage supports the full proposition, preserves material context and is adequate for the claim’s importance and scope.
How do I verify an AI-generated research summary?
Use a claim ledger containing the claim, source passage, scope, support status, caveat and final wording, then review the finished copy against that ledger.
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