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AI-generated documentation should be treated as a draft, not as evidence. To verify it, break the draft into individual claims, check each against current authoritative sources, reproduce technical steps where possible, record the results, and obtain qualified human review for consequential content. Good prose is not proof: the Government of Canada warns that generative AI can produce content that appears credible and reasonable but is inaccurate, nonsensical, or inconsistent with source data.
A reliable review pipeline makes it possible for another editor to see what was checked, what evidence supports each claim, what was actually tested, who approved high-impact material, and what remains unresolved. The steps below apply to AI-written help pages, API references, product guides, internal procedures, and other technical documentation.
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Can you trust documentation written by AI?
Not on the strength of its fluency. AI-generated text may be clear and plausible while misstating a requirement, inventing a citation, omitting an exception, or describing behavior that no longer matches the current product or regulation. Treat the output as material to verify, not a source that verifies itself.
There is no universal accuracy percentage that establishes whether AI-written documentation is safe to publish. A single score would also hide the differences between kinds of claims: a harmless wording suggestion is not equivalent to a security instruction, a medical warning, or a statement about legal compliance. Judge the document claim by claim, with review effort proportional to the harm an error could cause.
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Use stricter controls for safety, legal, medical, financial, security, and compliance material. Government of Canada guidance recommends checking generated content against trusted sources or having an expert review it. For scientific information, the USGS says validation, transparent methods, and rigorous peer review are important; its Fundamental Science Practices apply to AI-generated outputs as well as other scientific information.
What should a verification pipeline prove?
A review is complete only when it can show more than that links open or a reviewer skimmed the prose. It should establish that material claims have suitable evidence, that evidence supports the exact wording and scope, that instructions were tested where feasible, and that a responsible person made the publication decision.
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- Traceability: Can a reviewer connect each important assertion to the precise passage, test, or result that supports it?
- Reproducibility: Could someone else repeat the lookup or technical test using the recorded conditions?
- Risk coverage: Were security, privacy, bias, stale information, and likely failure modes considered where relevant?
- Auditability and freshness: Are decisions, artifacts, unresolved questions, and future review triggers retained?
A paragraph-level citation can help a reader find background, but it may not identify which sentence it supports or whether its qualifiers match. For consequential content, use a claim-to-evidence mapping instead.
How do I verify AI-generated documentation step by step?
Use the following workflow before publication. Keep the steps in order: defining the intended use determines the risk, and the risk determines how much evidence, testing, and review each claim needs.
1. Define the scope and the cost of being wrong
Record who will use the document, what they are trying to do, and the context in which the guidance applies. Capture the relevant jurisdiction, product or system version, and the date the content is meant to describe. If readers may apply the instructions in a different setting, state that boundary rather than silently broadening the claim.
Then identify plausible consequences of an error. A typo in a nonessential example may need an editorial correction; an incorrect permission setting, security recommendation, or legal requirement may need expert review and a test before release. Set the review threshold before looking for evidence, so the standard does not shift merely because a claim sounds convincing.
Write acceptance criteria in observable terms. For example, require that a setup procedure work in a clean environment on the named version, or that every statement about a regulatory requirement be supported by the applicable primary text. The criterion should describe what result counts as correct, not just ask whether the reviewer thinks the draft looks right.
This matters especially when evaluating AI systems. ISO/IEC TR 29119-11:2020 identifies “the main challenge being the test oracle problem”: reviewers can struggle to define the expected result against which a test should be judged. Clear acceptance criteria make the review less dependent on intuition.
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2. Split the draft into atomic claims
Build a claim ledger rather than reviewing only by paragraph. An atomic claim expresses one checkable point. Separate a sentence that bundles a version number, a compatibility promise, and an instruction into separate entries; each may require different evidence or a different test.
Include factual assertions, numbers, quotations, requirements, compatibility statements, code behavior, and recommendations. Label opinions, explanations, and procedural instructions separately so that reviewers do not mistake a persuasive rationale for a verified fact. Where a statement contains several conditions, preserve each condition in the ledger.
A useful ledger can be a spreadsheet or another inspectable record. Include at least these fields:
- Claim: The exact sentence or a concise, faithful statement of it.
- Risk: The likely impact if it is wrong, and why.
- Evidence: The exact source passage, test artifact, or result that supports it.
- Source context: Owner, publication or version date, geography, and retrieval date.
- Test result: Environment and outcome, or why no practical test was run.
- Review decision: Reviewer, status, rationale, and unresolved questions.
- Next review: A date or change trigger for checking it again.
Do not record only a URL or document title where the claim is important. A future reviewer needs to know which passage or result was relevant, not just where a broad collection of information can be found.
3. Retrieve primary evidence
For each claim, seek the source closest to the underlying authority or behavior: applicable regulations and standards, official vendor documentation, source code, reproducible test results, official statistics, or peer-reviewed work. A secondary article can point toward useful evidence, but it should not silently replace a primary source when the primary source is available and relevant.
Record source ownership, date or version, geography, and retrieval date. These details matter because a correct statement for one release, jurisdiction, or time may be incorrect for another. If the source does not establish a necessary qualifier, do not fill the gap with a confident-sounding inference.
Require a first-party source for every number and quotation. Confirm quotations word for word and keep their context; confirm that numbers use the right units, scope, and date. If the relevant material is unavailable or the authoritative source is ambiguous, mark the claim unresolved instead of treating a plausible secondary explanation as confirmation.
4. Check whether the evidence entails the exact claim
Open the cited passage and compare it with the complete claim, including qualifiers, exceptions, units, versions, and jurisdiction. Ask whether the source directly supports what the draft says, or whether it merely discusses the same subject. A citation that exists is not proof that it supports the sentence.
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Watch for common scope mismatches: a feature documented for one version described as universal; an optional behavior written as a default; a recommendation presented as a requirement; or an exception dropped from a summary. Also check that the wording does not make a stronger promise than the evidence—for example, turning a limited test result into a guarantee of compatibility.
Run automated checks for broken URLs and citation existence, but treat them as triage. A live address can lead to an irrelevant, stale, or contradictory page. Human entailment review is still needed to decide whether the evidence actually supports the claim.
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When documentation describes commands, API calls, configuration, calculations, or other technical behavior, test it in a controlled environment where feasible. Record the version, relevant setup, exact input, expected result, observed result, and any deviations. A clean environment helps reveal hidden assumptions such as a missing dependency or a setting left over from a previous run.
Use the acceptance criteria defined at the start to evaluate the result. A command that runs is not necessarily proof that the explanation around it is correct: check the stated effect, prerequisites, permissions, outputs, and failure conditions. If the step cannot safely or reasonably be executed, record the reason and identify what other evidence or expert review is needed.
For AI-generated or AI-related systems, avoid vague criteria such as “the answer should be good.” Define the expected outcome or acceptable range before testing, along with how failures will be classified. This addresses the test-oracle problem described by ISO/IEC TR 29119-11:2020.
6. Look for failure modes, not just confirmation
After checking the intended path, challenge the draft. Look for fabricated or mismatched citations, stale versions, missing caveats, ambiguous pronouns, unsupported numbers, insecure defaults, and steps that break when copied. Consider whether a reader could interpret an instruction differently from the author, or follow it successfully while reaching an unsafe result.
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NIST guidance for verification describes techniques that can include threat modeling, automated tests, static scanning, black-box tests, structural tests, historical tests, and fuzzing where applicable. These methods are not a mandatory checklist for every help page: choose them according to the system, claim, and risk. For example, security-sensitive code guidance may warrant threat-focused checks that a general explanatory page does not.
Keep negative results. If a citation fails to support a claim or a test exposes a mismatch, record it and either correct the wording, remove the claim, obtain better evidence, or assign follow-up work. Do not hide a failed check by replacing it with a broader statement that is harder to test.
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7. Obtain human sign-off for consequential claims
Assign a reviewer with relevant domain competence to high-impact claims. The reviewer should be able to assess the evidence and the consequences of error, not merely edit for style. A separate reviewer is particularly useful when the author or prompt designer has already invested in a particular interpretation.
Automation can prioritize suspicious claims, test links, or run technical checks, but it should not silently approve the content. Canada’s guidance points to trusted-source checking or expert review; USGS emphasizes validation, transparent methods, and rigorous peer review for scientific information. Record who reviewed consequential material, what they decided, and any limits on that decision.
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Keep an audit trail that lets another editor reconstruct what was reviewed. CMS guidance calls for citations, documented reasoning, and regularly updated documentation. A practical record includes the prompt or input version, model identifier when available, source links and passages, claim-to-source map, test evidence, reviewer, decision, and unresolved issues.
Store the record with enough structure to search and update it, rather than leaving key decisions only in chat history or an editor’s memory. NIST’s work on building evaluation probes into agentic AI describes authoritative documents being processed, cited reports being generated, citations being probed, and the results accumulated alongside the report in a machine-readable audit trail. The useful principle for documentation review is that evidence and evaluation results should remain inspectable beside the output they support.
Before publication, make unresolved status visible to the decision-maker. A claim marked “not checked,” “source unclear,” or “test not run” is different from a verified claim. Decide whether to remove it, narrow it, delay publication, or accept it with an explicit limitation and an assigned owner; do not let an empty ledger cell look like approval.
9. Set review dates and change triggers
Verification expires when its assumptions change. Set a review interval appropriate to the content and define triggers such as a model change, source revision, regulation update, dependency change, product release, or shift in operating context. The interval is a control chosen for the document’s risk and rate of change, not a universal period that works for every subject.
At re-review, revisit claims affected by the change rather than assuming an old approval still applies. The Intelligence Community framework asks how accuracy, bias, explainability, and drift will be checked over time. CMS guidance likewise emphasizes regular updates. Preserve the earlier decision and record what changed, so an editor can distinguish a new verification from a retroactive rewrite of the audit trail.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a claim ledger look like?
The ledger should be compact enough to maintain but specific enough to audit. This hypothetical example shows the level of detail; it does not describe a real product or verified behavior.
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| Field | Illustrative entry |
|---|---|
| Claim | “The setting is disabled by default in version 4.2.” |
| Risk | Medium; a reader may make a configuration decision based on the stated default. |
| Evidence | Exact passage in official version 4.2 documentation, or a reproducible observation if documentation does not establish the default. |
| Source context | Document owner, version/date, applicable environment or geography, and date the reviewer retrieved it. |
| Test result | Starting configuration and observed default in a clean version 4.2 environment, if tested. |
| Decision | Reviewer name or role, supported / revised / unresolved status, and reason. |
| Next review | Scheduled date or a trigger such as a new product version or source revision. |
Notice that the illustrative claim cannot be approved by finding any page that mentions the setting. The evidence must establish the default, the relevant version, and—if the wording implies it—the conditions under which that default applies. If the source describes a different release or a configurable state, revise the claim instead of stretching the evidence.
How much review does each kind of claim need?
Use the risk of error to choose the control, rather than applying one identical checklist to every sentence. The table is a decision aid, not a universal classification standard.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Claim or use | Review focus | Evidence and control |
|---|---|---|
| Low-impact explanation or wording | Check that it does not introduce factual or misleading implications. | Source check where it makes factual assertions; ordinary editorial review. |
| Version-specific feature or compatibility statement | Confirm release scope, exceptions, and current status. | Official version documentation and a reproducible test where feasible. |
| Command, API, configuration, or calculation | Check prerequisites, input, output, permissions, and failure behavior. | Controlled execution with recorded conditions and expected-result criteria. |
| Security, privacy, safety, legal, medical, financial, or compliance claim | Assess potential harm, jurisdiction, and implications of omissions. | Authoritative evidence plus qualified domain review; use targeted technical or adversarial checks when relevant. |
| Scientific conclusion or AI-system evaluation | Validate methods, assumptions, bias, explainability, and change over time. | Transparent methods, documented evidence, appropriate expert or peer review, and ongoing monitoring. |
The most demanding review is not automatically necessary for every sentence. It is necessary where the claim’s consequences, uncertainty, or pace of change justify it. Conversely, a disclaimer or a citation does not make a high-impact claim safe if its evidence has not been checked.
What are the common reasons AI documentation reviews fail?
Most avoidable failures come from treating surface signals as verification. Use this list during final review to catch process gaps:
- Trusting confident prose: Readability is independent of factual correctness. Check the claim against its evidence.
- Checking links but not support: Confirm both that a source exists and that its exact passage entails the claim.
- Combining multiple assertions: Split claims so a supported clause cannot conceal an unsupported one.
- Using evidence outside its scope: Match version, date, jurisdiction, units, exceptions, and conditions.
- Testing without an expected result: Define acceptance criteria before execution, especially for AI-system evaluation.
- Letting automation approve silently: Use automated checks to triage and test; retain accountable human review for consequential content.
- Discarding unresolved items: Mark uncertainty explicitly and remove, narrow, or assign the claim rather than leaving it implicit.
- Assuming yesterday’s approval remains valid: Recheck when sources, models, requirements, dependencies, product versions, or operating conditions change.
How do I decide whether the document is ready to publish?
Make the publication decision from the claim ledger, not from the draft’s overall polish. A document is ready when its material claims have adequate, scoped evidence; technical steps have been tested or their untested status is understood; required human reviewers have signed off; and unresolved issues have an explicit disposition.
Before release, confirm that the recorded evidence and tests correspond to the version being published, that readers can see material limitations, and that a next review date or change trigger is assigned. If a high-impact claim lacks support, publication should wait or the claim should be removed or narrowed. A tidy audit record is not a substitute for sound evidence; it makes the basis for the decision visible.
Frequently Asked Questions
Can an AI-generated citation count as evidence?
No. A citation suggested by a model is only a lead until a reviewer finds the source, checks its provenance, and confirms that the cited passage supports the claim. If it cannot be located or does not entail the wording, treat the claim as unsupported.
Should I disclose that documentation was written with AI?
The evidence here establishes the need for traceability and accountable review, not a universal disclosure rule. Follow any applicable organizational, legal, or publication requirements, and keep the production record sufficient to identify the input and model when available.
What if two authoritative sources disagree?
Record the conflict rather than selecting whichever source matches the draft. Check whether the sources cover different versions, dates, jurisdictions, or conditions, then ask an appropriate domain reviewer to resolve the scope or narrow the statement until it can be supported.
Can a reviewer approve documentation without running its commands?
Sometimes, if execution is unsafe or impractical, but the record should say that the step was not run and explain why. Use authoritative evidence and qualified review to assess it, and avoid presenting untested behavior as independently reproduced.
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