AI Content Review Checklist: Before Generated Copy Ships

Generated content is fluent, which is exactly why it needs checking rather than reading. This checklist covers what to verify, drawn from FISTA Solutions' AI enablement delivery work.

What is being checked?

Six categories, in order of consequence.

Category	Risk if missed
Factual claims	Published misinformation
Comparative claims	Substantiation exposure
Statistics and quotations	Fabricated detail
Brand voice and terminology	Inconsistent identity
Attribution and rights	Copyright exposure
Disclosure	Trust and regulatory issues

Factual accuracy

Check against sources, not against plausibility.

• Every factual claim traced to a source
• Dates, names, and figures verified individually
• Product capabilities checked against what the product does
• Technical statements reviewed by someone who knows the subject
• Claims about third parties verified
• Anything unverifiable removed rather than softened
• Sources recorded so the check can be repeated

Claims and substantiation

Anything comparative or quantitative needs evidence you could produce.

• Comparative claims identified and substantiated
• Performance figures traced to actual measurement
• Superlatives removed unless evidenced
• Customer outcomes verified and permission obtained
• Guarantees and commitments reviewed by legal
• Regulated claims checked against sector rules
• Substantiation filed where it can be retrieved later

Statistics and quotations

The categories most often fabricated.

• Every statistic traced to a named source
• Source checked to confirm it says what is claimed
• Publication date of each source checked for currency
• Quotations verified verbatim against the original
• Attributions confirmed to the right person and context
• Research citations confirmed to exist
• Anything that cannot be traced removed

Voice and terminology

Drifts toward generic without deliberate control.

• Style guide applied and checked
• Product and feature names correct and consistent
• Approved terminology used, banned terms absent
• Register appropriate for the audience
• Regional spelling and conventions correct
• Structural patterns not repetitive across pieces
• Reads as written by your organisation, not by anyone

Rights and attribution

Generated content can reproduce material closely.

• Content checked for close similarity to existing published work
• Images and media rights confirmed
• Third-party trademarks used correctly
• Quoted material properly attributed and within fair use
• Customer and partner references approved by them
• Licence terms of any referenced work respected
• Legal review where the content makes commitments

Disclosure and publication

Decided by policy, applied consistently. This is general guidance, not legal advice.

• Disclosure policy defined and applied
• Sector-specific content rules checked
• Accessibility requirements met including alt text
• Human reviewer named and recorded
• Review date recorded
• A correction process exists if an error is found after publication
• Published version matches the reviewed version

What are the most common failures?

Reading for quality rather than checking facts. Accepting statistics without tracing them. Comparative claims with no substantiation. Terminology drift. And deciding disclosure after someone asks about it.

Who should own this?

The content owner is accountable for what publishes under their name. A subject expert checks technical accuracy; legal reviews claims and commitments. Review by the person who generated it is not review.

How often should it run?

Every piece before publication, with a lighter check for low-risk internal content. Periodic sampling of published content catches errors that passed review.

What evidence should it produce?

Review records naming the reviewer and date, substantiation files for claims, and source lists for statistics. That set matters if a claim is ever challenged.

Can any of this be automated?

Partly. Terminology checks, style conformance, and detection of unsourced statistics can all be automated and should be, because they are tedious and catch real problems.

Factual verification and claim substantiation need a person, because they require knowing what is true rather than what is consistent. Automate the mechanical checks so reviewers spend attention where it matters. See AI evaluation checklist.

What should you do first?

Take a recently published generated piece and trace every statistic in it to a source. What you find determines how much this checklist is needed.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: factual claims traced to sources rather than read for plausibility, with mechanical checks automated so reviewers spend attention on substantiation, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To adapt this checklist to your environment, message FISTA on WhatsApp, or read AI evaluation checklist.