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AI assistance is not, by itself, proof of “AI slop.” The useful test is whether a piece shows care: accurate and current claims, traceable evidence, relevant detail, meaningful editing, and a person willing to stand behind the result. A 2025 JMIR Medical Education study describes slop as material made mostly or entirely with generative AI with little apparent human care for accuracy, fluency, helpfulness, or likely use. Researchers also note that there is still no agreed definition or measurement standard.
Use the five-pass check below to judge quality without pretending that style or a detector can prove who wrote something.
1. Run a claim pass
Start with the sentences that could be wrong, not with the writing style. Underline every factual claim, date, number, quotation, named organization, job title, product capability, and causal statement. Treat each item as unverified until you check it.
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- Find the primary source: an official document, regulator, court record, research paper, technical specification, or direct statement from the named organization.
- Check the publication or update date. A true statement can become misleading when a policy, price, version, or office-holder changes.
- Confirm the exact scope: country, edition, time period, sample, and conditions. Do not generalize a local or one-time result.
- Seek independent confirmation for consequential claims, especially figures, quotations, safety advice, and institutional roles.
For text produced with an AI assistant, this pass is mandatory. Every factual AI claim needs independent verification; fluent wording is not evidence.
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2. Run an evidence pass
Next, test whether the supporting material actually supports the sentence it follows.
- Open each link and confirm that it resolves to the cited material rather than a home page, search result, or unrelated document.
- Check whether the source says the same thing, with the same qualification. A study about one population cannot automatically support a claim about everyone.
- Look for precise citations: title, author or institution, date, and the relevant page, section, table, or quotation where practical.
- Separate evidence from commentary. A citation can support a fact without proving the writer’s interpretation or recommendation.
Missing, mismatched, or unusually obscure citations are warning signs. They are not proof that an AI system generated the passage; they are reasons to investigate the claims.
3. Run a specificity pass
Low-effort text often sounds polished while avoiding information a reader could use. Flag the following patterns and then verify them rather than treating them as an authorship test.
- Vague advice that could apply to any subject.
- Repeated points stated with different wording.
- Confident assertions with no example, data, method, or source.
- Links that are only loosely connected to the claim.
- Abrupt shifts in tone, detail, terminology, or assumed audience.
- Incorrect dates, names, titles, organizational roles, or technical terms.
- Generic introductions and conclusions that add no decision or evidence.
Specificity means more than adding numbers. A useful passage explains what happened, to whom, under what conditions, and what the reader should do with that information. A concrete example can demonstrate understanding; it cannot substitute for a source when the claim is consequential.
4. Run a human-care pass
Assess the work as an artifact, not just as a string of sentences. Look for signs that someone made responsible choices.
Signals of care
- Original reporting, direct observation, or a clearly described method.
- Examples and terminology tailored to the intended audience.
- Meaningful editing that removes errors, repetition, and irrelevant material.
- Disclosure of significant AI assistance when the context or publisher expects it.
- A named or identifiable person who can answer questions and correct mistakes.
- Added value beyond a restatement of easily found material.
Google’s quality guidance characterizes pages with little effort, originality, or added value as low quality and treats fabricated creator identities as deceptive. That is a quality and accountability assessment, not a rule that every AI-assisted page is unacceptable. A carefully edited, fact-checked piece with clear responsibility can be useful even when AI helped with drafting.
5. Run a cross-method pass
Only after the four quality checks should you consult automated signals. A detector score, watermark, or provenance record can add context, but none is conclusive.
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| Signal | What it can contribute | Why it cannot stand alone |
|---|---|---|
| AI-text detector | Highlights passages worth reviewing. | Scores are probabilistic and vary by detector, model, language, editing, and text length. |
| Watermark or provenance signal | May indicate that a particular generation or content pipeline was used. | Coverage is incomplete; transformations, copying, and unsupported formats can remove or prevent a signal. |
| Human or editorial inspection | Tests accuracy, usefulness, sourcing, originality, and accountability together. | Requires time and subject knowledge and still needs evidence for disputed claims. |
The UN has said that text analysis, black-box testing, and internal inspection can help, but none is sufficient alone. NIST likewise reports substantial variation among generators and detectors. The House of Commons Library summarizes the practical implication: “AI detection tools do exist, but they are unreliable and should not be treated as conclusive.”
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How to compare two pieces
When deciding which of two articles is more trustworthy, score the work on the same questions rather than asking which “sounds more human.”
| Comparison axis | Questions to ask |
|---|---|
| Evidence and traceability | Can each important claim be traced to a relevant, authoritative source? |
| Originality and added value | Does the piece provide reporting, analysis, examples, or decisions beyond a generic summary? |
| Specificity and relevance | Are details accurate and tailored to this audience, location, version, or use case? |
| Disclosure and provenance | Is significant AI assistance or the content’s origin explained where readers need to know? |
| Editing and accountability | Is there meaningful review and someone responsible for corrections? |
| Automated signals | Do detector or provenance results support the other evidence, rather than replace it? |
What a defensible finding looks like
Do not label a person or organization deceptive solely because prose feels synthetic or a detector reports a high score. Record the failed checks instead: for example, “Three dates conflict with the issuing agency’s records,” “The cited paper does not contain the claimed result,” or “The stated author and organization could not be verified.” Then explain what evidence supports your assessment and what remains uncertain.
A practical conclusion can use three categories:
- Verified and useful: material claims are supported, limitations are visible, and a responsible human review is evident.
- Needs revision: some claims or citations fail, but the problems are repairable through checking, sourcing, and editing.
- Unsafe to rely on: central claims are unsupported or false, sources are fabricated or mismatched, and no accountable review is apparent.
These categories describe reliability and care, not a definitive authorship verdict. Detection technology and platform guidance change quickly, so apply the current documentation for any tool you use and preserve the underlying sources and notes.
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