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The most reliable way to stop an AI agent from making unsupported claims is to audit the summary claim by claim. Give the agent relevant source passages, split its draft into atomic factual statements, attach evidence to each statement, test whether the evidence supports the entire wording, and preserve an “unresolved” or “abstain” outcome when it does not.
This process reduces unsupported assertions; it cannot guarantee that every sentence is correct. Language models can produce incorrect or misleading information, including fabricated citations and quotations, and a confident tone is not evidence of reliability. OpenAI’s guidance recommends verifying important facts against reliable sources.
Why fluent AI summaries still need a factuality audit
An AI-generated research summary can sound precise while quietly changing what a source says. Typical failures include adding a conclusion that the source never reached, turning a limited result into a universal rule, attaching a real citation to the wrong statement, or inventing a paper, quotation, statistic, or reference. The wording may be polished enough that a reader mistakes confidence for proof.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThe practical implication is that “Does this citation concern the same topic?” is not a sufficient test. A reviewer must ask whether the cited passage supports the complete claim, preserves its qualifications, and comes from a source adequate for the claim being made. This is the distinction behind the faithfulness, completeness, and sufficiency dimensions described by the National Institute of Standards and Technology (NIST).
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Use the workflow below for summaries that influence decisions, publish factual information, or report research findings. Treat it as a risk-control procedure, not as a promise of zero hallucinations.
What counts as an unsupported claim?
A claim is unsupported when the available evidence does not justify the statement at the strength, scope, or certainty in which it is written. The source may be relevant yet still fail to support the exact proposition.
Topical relevance is weaker than support
A source about a medication, model, population, or event can be topically relevant without proving a sentence about its effect. For example, a source that reports an association does not automatically support a sentence saying one factor caused another. A source that studies one country, year, or user group does not support an unrestricted claim about every country, year, or user group.
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| Test | Question to ask | What failure looks like |
|---|---|---|
| Faithfulness | Does the passage support the proposition the summary actually states? | The citation is about the subject but does not entail the claim, or the summary reverses the source’s direction or uncertainty. |
| Completeness | Does the summary preserve material qualifications, limits, and contrary findings? | A narrow result is presented without its population, dates, conditions, confidence limits, or exceptions. |
| Sufficiency | Is this source strong and appropriate enough for the claim? | A commentary or search snippet is used as proof of a technical, legal, medical, or numerical assertion that requires primary evidence. |
NIST presents these dimensions as evaluation probes under development, not as a finalized universal standard. They are nevertheless useful editorial checks because they force the reviewer to inspect the relationship between wording and evidence rather than merely count citations.
A claim-level workflow for defensible summaries
1. Retrieve authoritative, relevant material
Start with sources that directly address the question. Prefer primary studies, official records, standards, or first-party documentation when the claim calls for them. Keep the passages used for synthesis, not just the document titles or links. NIST describes a pipeline that evaluates document chunks for relevance before producing a cited report; retaining the relevant chunks makes later checking possible.
Define the intended scope before retrieval: the population, geography, time period, edition or version, and type of outcome. A source can be authoritative yet unsuitable if it covers a different scope. If the question concerns a current product version, for example, an older manual may explain the general feature but not establish its present behavior.
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2. Split the draft into atomic factual claims
Do not audit a paragraph as one unit. Rewrite each checkable assertion as a separate row. An atomic claim contains one principal proposition, such as who did what, when, where, and under what conditions. Split compound sentences when a single citation cannot support every clause.
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Keep interpretation and advice visibly separate from reported fact. “The study measured…” is a factual report that needs evidence. “A cautious editor should…” is a recommendation that should be presented as your procedure, not disguised as a finding from the study.
OpenAI’s factuality evaluation describes claim extraction as an initial stage: relevant factual claims are identified before they are checked against evidence. Applying the same discipline to your own draft prevents unsupported details from hiding inside fluent prose.
3. Build a claim ledger
A ledger makes omissions visible and creates an audit trail. Use one row per claim and record the exact passage, not a vague note that a source is “related.” A practical structure is:
| Column | What to record |
|---|---|
| Claim text | The smallest factual assertion that can be checked independently. |
| Source and passage | Document title, stable URL, page or section, and the quoted or closely paraphrased passage. |
| Source scope | Date, population, geography, version, conditions, and source type. |
| Support status | Supported, contradicted, insufficient, or unresolved. |
| Caveat needed | Any qualification required to keep the wording faithful. |
| Final wording | The sentence that will appear after the evidence check. |
Keep the original claim and the final wording in separate fields. That comparison reveals when editing has made a sentence stronger than the evidence. A ledger is an editorial implementation of the evaluation dimensions above; it is a control mechanism, not a published scientific metric.
4. Test whether the evidence supports the whole sentence
Read the cited passage and mark the exact words it establishes. Check the subject, action, direction, magnitude, date, and certainty. If the sentence says “caused,” the passage must support causation; an observational correlation is not enough. If it says “all,” “never,” or “proves,” look for evidence that warrants that breadth.
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When only part of a sentence is supported, split it, narrow it, or remove the unsupported clause. A citation placed at the end of a long paragraph does not automatically support every sentence in that paragraph.
5. Preserve scope and context
Carry the source’s boundaries into the summary. Record who was studied, where and when the work occurred, which version or intervention was tested, and what outcome was measured. Keep limitations and contrary findings when they change the reader’s interpretation.
Scope errors are often subtle. “Participants improved” may omit that the participants were volunteers in a short laboratory test. “The system reduced errors” may omit that the comparison involved particular models, prompts, or evaluation criteria. A faithful summary states the conditions next to the result instead of allowing readers to infer a broader guarantee.
6. Verify quotations, numbers, and references against originals
OpenAI warns that models can fabricate quotes, studies, citations, and references, and recommends checking quotations and data. Open the original document, locate the passage, and compare the wording character by character when the quotation is material. For numbers, verify the unit, denominator, date, rounding, population, and whether the value is a one-time result or a recurring figure.
If a quotation cannot be located, do not “repair” it by guessing. Remove the quotation, paraphrase only what the source supports, or mark the claim unresolved. The same rule applies to a paper title, author, DOI, table value, or benchmark score that cannot be verified.
7. Label the outcome instead of forcing a conclusion
Use explicit statuses:
- Supported: the cited passage entails the final wording at the stated scope.
- Contradicted: reliable evidence points in the opposite direction or directly conflicts with the claim.
- Insufficient: the source is relevant but does not establish the full proposition.
- Unresolved: the available material is ambiguous, inaccessible, conflicting, or missing.
For insufficient or unresolved rows, narrow the sentence, state the uncertainty, seek better evidence, or omit the claim. OpenAI’s factuality work allows an “unsure” result, and its person-factuality evaluation permits abstention when uncertainty is too high. An explicit abstention is safer than a plausible invention.
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8. Review the finished summary against the ledger
After editing, compare every material factual sentence with its row. Confirm that no new number, adjective, causal verb, time qualifier, or universal quantifier appeared without a check. Ensure each link points to the source actually used and that the final wording still includes the caveat recorded in the ledger.
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Use this sequence for each citation:
- Locate the cited source. Follow the link or open the named document. A citation that cannot be found is not usable evidence.
- Find the cited passage. Search for the relevant section, table, footnote, or page. Do not rely on a search-result snippet or the abstract alone when the claim depends on methods or limitations.
- Rewrite the passage in plain language. State exactly what the source establishes without adding implications.
- Compare propositions. Match the claim’s subject, action, direction, magnitude, population, date, and certainty to the passage.
- Check omitted context. Look for exclusions, confidence intervals, alternative explanations, sample limits, and authors’ stated uncertainty.
- Assign a status. Mark the row supported, contradicted, insufficient, or unresolved, then change the sentence accordingly.
Consider this illustrative example: a source says that a small sample showed an association during a six-week observation period. A draft says, “The intervention permanently causes improvement for everyone.” The citation is topically relevant, but it fails faithfulness because association became causation, completeness because the duration and sample were dropped, and sufficiency because the evidence cannot establish a universal or permanent effect. A defensible revision would retain the observed association, sample, period, and uncertainty.
Handling ambiguity, missing evidence, and conflicting sources
When information is unavailable
Say what could not be established. “The available source does not report the denominator” is more informative than inventing one or silently presenting a percentage. If a document is paywalled, unavailable, or missing a method detail, either find an accessible authoritative source or qualify the claim to match what is actually visible.
When sources disagree
Do not average conflicting findings into a synthetic conclusion. Record each source’s date, population, method, and outcome, then explain the disagreement at that scope. If one source is more appropriate for the question, say why; otherwise report that the evidence is mixed or unresolved. A later or larger study is not automatically decisive without checking design and applicability.
When the agent is asked for a single answer
Require the agent to return “insufficient evidence” or “unresolved” when the ledger cannot support a decisive statement. This is a safety behavior, not a failure to complete the task. The summary can still explain what is known, what is disputed, and what evidence would resolve the question.
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Verification approaches compared
Different review systems vary in what they inspect. Compare them on the dimensions that affect auditability, not on the number of citations they print.
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| Approach | Evidence granularity | Support judgment | Uncertainty handling | Auditability | Scope control |
|---|---|---|---|---|---|
| Whole-document review | Checks a document or section as a broad unit. | May establish topical relevance without testing each proposition. | Often leaves uncertainty implicit. | Reviewer may struggle to identify which passage supports which sentence. | Dates, populations, and conditions can disappear during synthesis. |
| Passage-level review | Links a sentence to a specific paragraph, table, or page. | Can test whether the passage entails the wording. | Can retain explicit limitations if the passage is captured. | Provides a traceable source location. | Better, provided the reviewer records scope next to the passage. |
| Claim-level ledger | Audits each atomic factual assertion. | Separates faithfulness, completeness, and sufficiency judgments. | Supports supported, contradicted, insufficient, and unresolved labels. | Lets another reviewer reproduce the decision and rationale. | Requires scope fields so the claim cannot silently broaden. |
Retrieval and citations make inspection easier, but a citation alone does not prove support. The support relationship still needs a human or automated check against the source passage.
Controls to put in an agent’s instructions
Prompting cannot make a model infallible, but clear output requirements make unsupported claims easier to detect. Ask the agent to:
- Use only the supplied or retrieved sources for factual assertions.
- Return a claim ledger before or alongside the prose summary.
- Quote or identify the exact passage supporting each material claim.
- Separate reported findings from interpretation and recommendations.
- Preserve population, date, geography, version, method, and limitation qualifiers.
- Mark a claim unresolved when the passage is missing, ambiguous, or contradictory.
- Never invent a citation, quotation, statistic, page number, or study detail.
- Run a final comparison between the published wording and the ledger.
A compact instruction block can be adapted to your system:
For every factual statement, create one claim-ledger row. Record the source passage, source scope, and support status. Test faithfulness, completeness, and sufficiency. If the evidence does not establish the wording, narrow it or mark it unresolved. Do not guess missing numbers, quotations, citations, dates, or references. In the final summary, keep factual claims separate from interpretation and advice.Use a reviewer to sample high-risk rows even when an automated evaluator is available. Prioritize claims involving safety, health, law, money, identity, causation, numerical comparisons, and current status.
How to interpret hallucination and factuality metrics
Model evaluations can inform a deployment decision, but they are not a probability that an individual summary is correct. The GPT-5 System Card reports a 26% smaller hallucination rate for GPT-5-main than GPT-4o under the system card’s stated evaluation, and a 65% smaller hallucination rate for GPT-5-thinking than OpenAI o3 under its reported comparison. Those figures are tied to the tested models, prompts, dataset, definition, and conditions; they should not be generalized into a universal error rate.
The same system-card material describes claim-level and response-level factuality measures. A claim-level measure can reveal that one answer contains several unsupported statements even when the overall response appears useful. Before citing any chart value, verify its population, denominator, metric definition, and evaluation setting in the underlying system card. Do not use a benchmark percentage as a substitute for checking the citations in your own summary.
OpenAI also states that accuracy remains below 100% on challenging evaluations and that hallucinations continue to be a problem. The operational conclusion is straightforward: choose a model and retrieval setup that fit the risk, then retain claim-level verification for consequential work.
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| Failure mode | Why it happens | Fix |
|---|---|---|
| A real citation is attached to an unsupported sentence. | The agent retrieves a related source and treats relevance as proof. | Open the passage and test the entire proposition; split or narrow the sentence. |
| A caveat disappears. | Compression removes sample, date, method, or limitation details. | Store scope and caveats in the ledger and require them in final wording. |
| A correlation becomes a cause. | The draft uses stronger verbs than the study design permits. | Match causal language to the source’s design and stated conclusion. |
| A quotation or statistic is fabricated. | The model predicts plausible text when retrieval is incomplete. | Verify against the original; remove or mark unresolved if it cannot be located. |
| A benchmark becomes a product guarantee. | A result from one evaluation is generalized to every task. | Name the models, test setting, metric, and limitations next to the figure. |
| Conflicting evidence is hidden. | The agent optimizes for a single fluent answer. | Record the disagreement and report what remains unresolved. |
| New unsupported wording appears during editing. | Headlines and transitions are written after the evidence check. | Run a final sentence-to-ledger comparison, including headings and captions. |
A publication checklist
- Every material factual statement has an identifiable source passage.
- Each citation supports the complete proposition, not merely the topic.
- Numbers include their unit, population, date, denominator, and test conditions where relevant.
- Quotes, study details, references, and links were checked against originals.
- Scope qualifiers and material limitations survived summarization.
- Interpretation and advice are labeled as such rather than presented as findings.
- Contradicted, insufficient, and unresolved claims are not written as settled facts.
- Model or benchmark percentages include their evaluation context and are not treated as guarantees.
- The final text was compared with the claim ledger after the last edit.
This workflow changes the agent’s job from “write a convincing summary” to “write only what can be traced and defended.” That shift—atomic claims, passage-level evidence, explicit scope, and permission to abstain—is the practical way to reduce unsupported claims while keeping useful uncertainty visible.
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