Freeze the artifact and the responsibility boundary
Save the draft version, brief, audience, intended decision, AI tool and mode, supplied sources, prompt or workflow version, author edits, prohibited claims, commercial relationships, and deadline. Name the factual reviewer, subject specialist when needed, editor, and final approver. The two-pass editorial review sheet prevents a moving draft from receiving fragmented approval. Human-in-the-loop is meaningful only when a person receives enough evidence, time, authority, and a working stop path. NIST's framework emphasizes defined human roles; the practical consequence is that reviewed by human cannot be a decorative status applied after automation has already published.
Evidence: National Institute of Standards and Technology; National Institute of Standards and Technology
Pass one atomizes factual and source-dependent statements
Highlight every date, number, quotation, rule, product behavior, research finding, causal statement, comparison, superlative, testimonial, and high-stakes recommendation. Map each to an accessible source passage, version, date, jurisdiction, method, and qualifier. Verify citation existence and metadata externally, then test whether the passage supports the precise wording. PMLR research documents reference hallucination under tested models, which is a reason for direct inspection rather than a claim that every citation fails. Split compound sentences, label inference, recalculate simple figures, and remove claims whose support cannot be reconstructed. Do not allow copyediting to smooth uncertainty out of a verified sentence.
Evidence: ACM CHI; Proceedings of Machine Learning Research
Challenge the source set and the model's synthesis
Search for corrections, superseding pages, counterevidence, methodological limitations, population mismatch, conflicts of interest, and copied reporting. Trace secondary claims to their origin and count evidence chains rather than URLs. Check whether the model joined facts into a conclusion no source makes, invented a timeline, or treated silence as consensus. For current technical or regulatory material, confirm the page on the review date. For legal, medical, financial, or safety content, require suitable professional review or reduce the claim. Record rejected evidence and contradiction decisions so later generations do not reintroduce the same unsupported language.
Evidence: National Institute of Standards and Technology; National Institute of Standards and Technology
Pass two asks whether the article earns the reader's time
Read without the source ledger. Does the opening state the real problem, do sections follow the reader's decisions, and does each paragraph add explanation, evidence, example, limitation, or next step? Remove generic filler, repeated templates, false urgency, unsupported certainty, and claims that exist only to place a product link. Check headings, description, accessibility, terminology, examples, disclosure, quotation limits, and the difference between illustrative and observed outcomes. The CHI human-AI guidelines emphasize expectation and correction in interaction; editorially, the article should tell readers what AI contributed, what humans verified, what remains uncertain, and how to challenge an error.
Evidence: National Institute of Standards and Technology; ACM CHI
Review the commercial path as part of editorial integrity
Verify that affiliate or advertising relationships are clear, proximal where needed, and do not determine inclusion, ranking, or unsupported praise. Product mentions should follow published criteria, current official evidence, and a reader-fit statement. Add alternatives such as doing nothing, using a simpler process, or seeking a qualified professional. Inspect link destination, merchant identity, price or feature volatility, and whether the landing page changes the promise. Separate editorial evidence from tracking parameters. A disclosure does not cure a misleading claim. The approver should be able to remove every commercial link without collapsing the article's educational value.
Evidence: National Institute of Standards and Technology; ACM CHI
Record the decision and reopen triggers
The next action is to review one pending article in evidence-first and reader-value passes by different people or separated sessions, then reconcile findings. Record pass, revise, or stop for every material issue, the final version hash, approver, date, source access dates, and next review. Limits remain: reviewers share biases, time pressure creates automation complacency, expert judgment can disagree, and web sources change. Reopen after a source correction, policy or product change, credible reader challenge, workflow change, or material edit. Publication is approved only when the named human can explain and defend the final claims—not when the model has generated a convincing rationale.
Sources and further reading
These references informed this article. A source supports a claim; it does not imply endorsement of TenMultigure or any future product reference.
- AI Risk Management Framework CoreNational Institute of Standards and Technology · Accessed August 10, 2026
NIST AI RMF Core grounds defined human-AI roles, context, impacts, measurement, documentation, and oversight proportionate to the use case.
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence ProfileNational Institute of Standards and Technology · Accessed August 10, 2026
NIST's Generative AI Profile supports enhanced review for confabulation, information integrity, privacy, harmful content, and human-AI configuration risks.
- Guidelines for Human-AI InteractionACM CHI · Accessed August 10, 2026
The independent CHI guidelines support designing human-AI interaction around expectations, context, correction, feedback, and user control across time.
- Citation Constraints and Reference Hallucinations in Large Language ModelsProceedings of Machine Learning Research · Accessed August 10, 2026
The independent PMLR citation study motivates direct metadata and source checks instead of assuming polished references establish factual reliability.
Reviewed by TenMultigure AI Editorial Safety Review. See an error or a source that has changed? Tell the editorial team.
Review method: AI-assisted desk research with editorial checks. Reviewed ; next scheduled review . Built a two-pass review method that separates claim and source integrity from structure, reader value, tone, accessibility, commercial balance, and approval.