# AI hallucinations and fact\-checking: a claim\-by\-claim workflow

Canonical: https://namedproof.com/knowledge/en/hallucination-and-fact-checking

By NamedProof (Organization) — https://namedproof.com/about

Published: 2026-10-17 · Sources checked: 2026-10-17 · Language: en

Evidence and evaluation

Check AI\-generated factual claims against appropriate sources, preserve uncertainty, and distinguish unsupported assertions from verified errors\.

To fact\-check an AI answer, break its material assertions into claims, identify the appropriate evidence for each and compare the actual supporting passages\. Mark unavailable or inconclusive evidence explicitly\. A confident tone, plausible citation or repeated answer is not a substitute for verification, and a successful spot check does not validate the rest of a document\.

## Identify what kind of problem you are checking

An answer may invent a fact, misread a source, omit a condition or apply an old rule to a new situation\. These failures can look similar in fluent prose\. Start with the reader decision: which statement could change what someone chooses or does? Prioritise those claims while keeping the limits of any partial review visible\.

OpenAI describes hallucinations as plausible false statements and discusses how evaluation incentives can reward guessing rather than admitting uncertainty\. That explanation supports taking uncertainty seriously; it does not supply a single error rate for every current model or every question\. (S11)

## Turn a paragraph into claims you can inspect

Separate names, dates, quantities, comparisons and causal conclusions\. A sentence saying a tool is faster, safer and cheaper makes several claims with different evidence needs\. The same source may not support all of them\. Record the comparison baseline and the conditions under which each characteristic was measured\.

FActScore illustrates an atomic\-fact approach to factual precision in long\-form generation\. Its original work includes generated biographies\. The general editorial lesson is to inspect individual assertions instead of assigning one vague true\-or\-false label to a mixed paragraph\. That does not make our manual workflow a reproduction of the benchmark\. (S28)

## Choose evidence suited to the statement

A current product feature needs current product documentation or a direct functional check\. A research claim needs the original paper, with methods and results when the statement depends on them\. A legal or medical claim may require current jurisdiction\-specific or clinical expertise\. A search snippet can identify a source to inspect, but should not silently stand in for the document when missing context matters\.

Follow the actual URL\. Check that its title, authors and date match the citation, then locate the supporting passage\. If access fails, say what could and could not be read\. An inaccessible source is not automatically false, but it cannot be used as if its full contents were verified\.

## Keep verified error separate from missing evidence

The following labels are a practical review vocabulary for this guide\. They are not a claim that an automated system can determine truth in every case\. A reviewer should preserve the source and reasoning behind each label\.

Proposed claim\-check record; use the label supported by the inspection\.

| Finding | Meaning | Next action |
| --- | --- | --- |
| Supported within scope | Inspected evidence supports the statement and conditions | Keep the conditions with the claim |
| Confirmed error | Evidence directly contradicts a material assertion | Correct every affected occurrence |
| Unsupported extension | Conclusion goes beyond available evidence | Narrow or supply appropriate evidence |
| Not verified | Evidence is unavailable, incomplete or conflicting | State the gap and investigate |

## Do the arithmetic and inspect the inference

In a fictional comparison, an AI answer reports that a task fell from ten minutes to six minutes and therefore saves 40% of all staff time\. The task\-duration calculation is \(10 − 6\)/10 = 40%\. The staff\-time conclusion needs additional evidence about how often the task occurs, what work remains and whether the measured conditions apply\.

A careful correction preserves the useful calculation while narrowing the inference: task duration fell by 40% in the described example\. It does not claim a measured saving for an actual customer\. This is why checking the numbers and checking what the numbers mean are separate jobs\.

## Correct the whole family of repeated claims

A material correction should reach the opening, body, tables, summary and any translated or machine\-readable equivalent\. Leaving the old claim in a short answer makes it easy to repeat after the long section has been fixed\. Record a substantive update date and what changed; do not change dates merely to suggest freshness\.

Where an API provides citation locations, those locations can make evidence easier to inspect\. Anthropic documents page, character or block references for supplied documents\. The reference mechanism helps trace a claim; the reviewer still needs to judge support, scope and source reliability\. (S09)

## Use human review with a defined scope

A real specialist can add domain judgment, but a reviewer name alone does not establish that every claim was checked\. State who reviewed what, which version, against which materials and with what unresolved limitations\. An AI editing pass should never be presented as a human expert's approval\.

Finish with a claim log that another person can inspect\. Important gaps may justify withholding an actionable recommendation until the relevant evidence is available\. Stating the unresolved claim gives the reader a clear reason to pause or seek better evidence\.

## Method

NamedProof editorial guide, prepared with AI assistance from primary sources and an original practical analysis\. It is not a commissioned expert assessment and has not been personally reviewed by a named subject specialist\. Sources were checked on 17 October 2026\.

## Original contribution

Original contribution: a four\-label fact\-checking workflow and a fictional calculation separating task\-time arithmetic from an unsupported staff\-time inference\.

## Limitations

- This guide does not constitute legal, medical or other regulated professional advice\.
- The worked time\-saving example is hypothetical, not a client result\.
- No review method eliminates all error; the scope and unresolved claims must remain visible\.

## Sources

### S09: Citations

https://platform.claude.com/docs/en/build-with-claude/citations

Sources checked: 2026-10-17

Claude API supplied\-document citation feature\. A valid source location does not independently prove that every inference is correct/fully supported; not public\-web brand selection evidence\.

### S11: Why language models hallucinate

https://openai.com/index/why-language-models-hallucinate/

Sources checked: 2026-10-17

Authors analysis of language\-model evaluation\. Not a universal present\-day hallucination rate; benchmark percentages need exact model/task conditions\.

### S28: FActScore: Fine\-grained Atomic Evaluation of Factual Precision

https://arxiv.org/abs/2305.14251

Sources checked: 2026-10-17

Original evaluation, including generated biographies\. Precision differs from completeness, expertise and independence\. Historical rates are not current model performance\.

## Related guides

- [How to verify sources and citations, one claim at a time](https://namedproof.com/knowledge/en/verify-sources-citations)
- [Research methods, uncertainty and limitations: how to read a review](https://namedproof.com/knowledge/en/research-methods-uncertainty-limitations)
- [How to evaluate an LLM for the job you need it to do](https://namedproof.com/knowledge/en/llm-evaluation)
- [RAG and citation quality: check retrieval, support and completeness](https://namedproof.com/knowledge/en/rag-citation-quality)

## Available languages

- [English](https://namedproof.com/knowledge/en/hallucination-and-fact-checking)