Form: AI-Assisted Content Authoring and Verification Record

This is a ready-to-use record that captures how a piece of AI-assisted regulatory or CMC content was produced and verified, sufficient to reconstruct and defend it. It is the evidence that a named human authored and owns the content, that every claim traces to a verified source, and that the high-risk specifics (numbers and citations) were checked individually. Complete one record per AI-assisted deliverable that becomes regulatory output. Replace every <<FILL: ...>> placeholder with your own specifics. A filled specimen follows. Verify each cited regulation against the current source before you rely on it.

Record control

Field	Entry
Record title	AI-Assisted Content Authoring and Verification Record
Document number	`<<FILL: FORM-ID, e.g. FRM-AIRA-003>>`
Version	`<<FILL: version>>`
Content deliverable	`<<FILL: e.g. Nonclinical written summary, Module 2.4>>`
Program / product	`<<FILL>>`
Linked controlled document	`<<FILL: the DMS record for the final content>>`

1. Use classification

Field	Entry
Tier	`<<FILL: internal intelligence / assistive draft / conformance check>>`
One-sentence classification	`<<FILL: output, what it feeds, who owns it>>`
Named author (accountable)	`<<FILL>>`

2. AI system used

Field	Entry
System / service	`<<FILL>>`
Model version (pinned?)	`<<FILL>>`
Prompts / configuration reference	`<<FILL>>`
Guardrails applied	`<<FILL: e.g. must cite source and page for each statement>>`
Sources grounded in (retrieval set)	`<<FILL: list the exact source documents>>`

3. Claim verification

Record the verification of the content against source. Every factual claim must trace to a verified source; expand the table to cover the deliverable.

Claim / statement	Source (document, page)	Verified against source?	Corrected?	Verifier initials
`<<FILL>>`	`<<FILL>>`	`<<FILL: Yes/No>>`	`<<FILL: Yes/No + note>>`	`<<FILL>>`
`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`	`<<FILL>>`

4. High-risk specifics check

Numbers and citations are the characteristic AI failure. Confirm each was checked individually, not skimmed in context.

Item type	Count in content	All verified against source?	Notes
Numeric values (limits, results, doses, dates, identifiers)	`<<FILL>>`	`<<FILL: Yes/No>>`	`<<FILL>>`
Regulatory citations (guidance, regulation, study IDs)	`<<FILL>>`	`<<FILL: Yes/No>>`	`<<FILL>>`
Statements flagged uncertain by the model	`<<FILL>>`	`<<FILL: resolved from source?>>`	`<<FILL>>`

5. Author attestation

Field	Entry
I authored and own this content; the AI was an assist	`<<FILL: author name>>`
Trained on the AI’s known failure modes	`<<FILL: Yes, date of training>>`
Extent of editing performed	`<<FILL: substantive / minor + note>>`
Author signature and date	`<<FILL>>`

6. Approval

Role	Name	Signature	Date
Named author	`<<FILL>>`
QA / Regulatory approver	`<<FILL>>`

7. Records note

Retain this record with the controlled document for the final content, for the applicable records-retention period. Keep the drafts and source links in the controlled document system, not in the AI tool’s chat history.

8. References

21 CFR Part 11; ALCOA+ data-integrity attributes (accurate and original in particular). FDA draft guidance on AI to support regulatory decision-making (January 2025, draft); EU draft Annex 22 on artificial intelligence (7 July 2025, not in force).

Confirm the current version and status of each reference before issue.

Filled specimen

The following shows sections 3 and 4 completed for an example nonclinical summary, so you can see the expected detail. The values are illustrative; replace them with your own.

Claim / statement	Source (document, page)	Verified?	Corrected?	Verifier
No-observed-adverse-effect level was 30 mg/kg/day in the 13-week rat study	Study report STR-114, p. 88	Yes	No	J.S.
The pivotal study identifier is STR-114	Study report STR-114, cover	Yes	Yes, model had written STR-141 (transposed)	J.S.

Item type	Count	All verified?	Notes
Numeric values	22	Yes	Each dose, duration, and NOAEL checked against the cited report individually
Regulatory citations	3	Yes	One guidance number the model asserted was wrong and was corrected against the source
Statements flagged uncertain by the model	2	Yes	Both resolved from the source report by the writer

In this example the record caught two characteristic AI errors, a transposed study identifier and a wrong guidance number, precisely because the writer verified the numbers and citations individually rather than trusting the fluent prose. That claim-by-claim check, recorded, is what makes the assisted content defensible.

Common inspection findings this record prevents

An unverifiable claim in submitted content, because no one recorded a source-by-source check.
A transposed number or invented citation that slipped through fluent text.
No evidence that a named human actually authored and verified the content.
No record of the AI system, version, or grounding, so the process could not be reconstructed.

How to adapt this form

Expand the claim-verification table to cover the whole deliverable, not a sample.
Insist the high-risk specifics count is real, and that every number and citation was checked one by one.
Require the author attestation to name the extent of editing; a “minor” edit on a whole section is a signal to look harder.
File the record with the controlled document for the final content.
Confirm every reference against the current published version and status before issue.