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Do not publish AI-written documentation on prose quality alone. Treat the draft as a set of claims that must be traced to current, authoritative evidence, tested in the stated environment, and approved by a qualified human when an error could cause harm. The workflow below turns a plausible-sounding draft into an auditable document: define risk, split the text into atomic claims, retrieve primary sources, check that each source actually entails its claim, execute technical steps, run adversarial tests, obtain sign-off, preserve provenance, and re-review after material change. There is no universal accuracy percentage for AI-generated documentation, so a claim-level record of evidence and test results is more useful than a broad model score.
Why can AI documentation sound right and still be wrong?
Generative systems optimize for a coherent continuation, not for truth. The Government of Canada warns that generative-AI output can appear “well developed, credible and reasonable” while being inaccurate, nonsensical, or inconsistent with source data. Fluent wording can therefore hide a wrong version number, an invented option, an omitted exception, or a command that fails when copied.
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The danger rises with the consequence of an error. A typo in an internal how-to may waste minutes; an incorrect security setting, medical instruction, legal interpretation, financial calculation, or compliance requirement can cause material harm. Documentation also decays: products, dependencies, regulations, operating conditions, and models change. Verification is a control process, not a one-time reading exercise.
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How do I define the scope and risk before checking an AI draft?
Start by recording what the document is for and what an error would do. This determines how much evidence, testing, and human review each section needs.
Capture the operating context
- Audience: Identify the role, skill level, and language assumptions of the reader.
- Intended use: State whether the text is educational, operational, customer-facing, or used to make a regulated decision.
- Jurisdiction: Record the country, state, industry regime, or contractual boundary that controls the advice.
- Version date: Note the product, API, operating system, standard, regulation, or dependency version covered.
- Harm threshold: Describe the consequence of a wrong claim, including safety, privacy, security, financial, legal, medical, or compliance impact.
Use stricter controls for high-impact content. A low-risk explanation can receive a lighter editorial pass, while a security hardening guide should require reproducible tests and domain-expert approval. Keep the scope statement with the document so a later reviewer can tell whether the evidence still applies.
Write acceptance criteria before testing
Define what a correct result looks like before running a command or checking an answer. ISO/IEC TR 29119-11:2020 calls the difficulty of knowing the expected result the “test oracle problem.” If reviewers cannot state the expected output, status code, file change, calculation, or safety condition, they cannot reliably accept or reject the AI’s instruction. Express criteria in observable terms, such as the exact configuration value, returned field, exit status, or permitted range.
How do I split AI-generated prose into verifiable claims?
Convert paragraphs into atomic assertions. An atomic claim contains one fact or action that can be supported or tested without relying on a neighboring sentence.
Separate factual assertions, numbers, requirements, compatibility statements, code behavior, and recommendations. Mark opinions, examples, and procedural instructions separately; they require different checks. Split a sentence such as “Version 4 supports encrypted exports by default and reduces storage use by 30 percent” into separate claims for version support, the default setting, and the measured reduction. Each may have a different source and different confidence.
Build a claim ledger
A ledger makes review auditable and prevents a citation attached to one sentence from being mistaken for support for an entire paragraph.
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| Ledger field | What to record |
|---|---|
| Claim text | The smallest factual or procedural assertion, copied or paraphrased precisely. |
| Risk | The potential impact if the claim is wrong, with high-impact categories flagged. |
| Source passage | The exact paragraph, table, requirement, code line, or test result that bears on the claim. |
| Source date and version | Publication or release date and the edition, revision, or product version. |
| Geography or jurisdiction | The territory, market, regulator, or environment in which the evidence applies. |
| Test result | Environment, inputs, observed output, and whether the acceptance criterion passed. |
| Reviewer | The person who checked the evidence or executed the test, including domain qualification when relevant. |
| Status | Supported, contradicted, or unresolved, with the decision and next action. |
| Next review date | The scheduled check or event that will force an earlier review. |
Give every number and quotation its own row. If one source supports only part of a compound statement, narrow the wording or add evidence for the remaining parts. Keep unresolved claims visible rather than allowing a polished paragraph to conceal them.
Where should I look for evidence for AI-written documentation?
Prefer first-party, current, scoped evidence. The strongest sources are regulations and standards, vendor documentation, source code, reproducible test results, official statistics, and peer-reviewed work. A secondary summary can help locate a source, but it should not be the final authority for a consequential claim.
Record source identity and scope
For every source, capture its owner, publication or revision date, version, geography, and retrieval date. Check whether the passage describes the same edition, product tier, operating system, hardware, or jurisdiction as the draft. A current statement for one region may be wrong elsewhere; a feature documented for a preview release may not exist in the stable release.
Check that citations exist before judging them
Run URL and citation-existence checks to catch dead links, malformed references, and citations that point to a document with no matching section. Existence is only a screening step. A live link can still be irrelevant, out of date, or too general to support the sentence attached to it.
How do I check whether a source actually supports each claim?
Perform an entailment check: read the cited passage and ask whether it supports the exact wording, not merely a related topic. Confirm qualifiers, exceptions, units, thresholds, versions, and jurisdiction.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Locate the controlling passage. Identify the sentence, table row, requirement, code path, or measured result that is supposed to support the claim.
- Compare scope. Check subject, action, conditions, population, geography, version, and time period. A statement about “may support” cannot be rewritten as “supports,” and a laboratory result cannot be presented as a universal guarantee.
- Check completeness. Look for omitted prerequisites, limits, exclusions, security warnings, and failure conditions.
- Check the number and unit. Verify whether a percentage, date, limit, or performance figure is measured, estimated, one-time, recurring, or promotional.
- Record the decision. Mark the claim supported, contradicted, or unresolved, and explain what must change in the text or evidence.
NIST describes a similar pattern for AI evaluation: process authoritative documents, generate cited reports, probe the citations, and retain the results in a machine-readable audit trail. Apply that discipline at the claim level rather than treating a bibliography as proof.
Rank #3
How do I test commands, configurations, and calculations in an AI draft?
Reproduce technical instructions in a controlled environment instead of accepting a plausible explanation. Use a disposable system, isolated account, test data, or version-pinned container when the action could alter production state or expose sensitive information.
- Freeze the inputs. Record the model output, prompt or source input, software versions, configuration, operating system, and test data.
- Define the oracle. Write the expected exit status, API response, file change, calculation, or safety property before execution.
- Run the smallest safe example. Start with a non-destructive command or sample payload that exercises the documented behavior.
- Compare observed and expected results. Capture logs, screenshots, response bodies, generated files, and error messages; do not rely on memory.
- Repeat where variability matters. Re-run with boundary values, fresh sessions, different permissions, or another supported version when the claim implies broad compatibility.
- Attach artifacts to the ledger. Link the test record to every claim it verifies and note any deviation or workaround.
For calculations, show inputs, formula, units, rounding, and a separately checked result. For API or configuration guidance, verify authentication, permissions, defaults, error handling, and rollback behavior—not just the happy path.
What adversarial checks catch mistakes that normal reading misses?
After ordinary evidence and execution checks, deliberately search for failure modes. NIST verification guidance includes threat modeling, automated tests, static scanning, black-box tests, structural tests, historical tests, and fuzzing where applicable.
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- Fabricated citations: Confirm that every reference exists and that the quoted passage is present.
- Stale versions: Compare release dates and deprecation notices with the version named in the article.
- Omitted caveats: Search the source for warnings, exceptions, prerequisites, and limits that the draft leaves out.
- Ambiguous language: Replace vague pronouns, undefined terms, and words such as “always,” “secure,” or “guaranteed” unless evidence warrants them.
- Unsupported numbers: Trace each figure to a first-party measurement or remove it.
- Insecure defaults: Inspect permissions, credential handling, network exposure, logging, and data retention.
- Copy-and-paste failure: Run commands exactly as printed, including punctuation, indentation, shell, and platform assumptions.
- Boundary behavior: Test empty, maximum, malformed, expired, and unauthorized inputs when the procedure handles data or access control.
Historical tests are useful when a claim describes a recurring behavior: compare the current result with earlier known-good runs and explain any change. Fuzzing or broad automated probing belongs in a controlled environment and should be proportionate to the risk.
When is human review required for AI-generated documentation?
Automation can triage claims and execute repeatable checks, but it should not silently approve consequential content. Assign a domain-qualified reviewer whenever the document affects safety, law, medicine, finance, security, privacy, regulated compliance, or scientific conclusions.
The reviewer should inspect the highest-risk claims first, verify that the evidence matches the stated scope, examine failed and unresolved tests, and sign a recorded decision. Government of Canada guidance recommends checking generated content against trusted sources or having an expert review it. The U.S. Geological Survey states that adherence to its Fundamental Science Practices ensures scientific information, including AI-generated outputs, is reliable and accurate under Survey Manual SM 502.4; that principle requires transparent methods and rigorous peer review rather than trust in fluent prose.
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Make sign-off specific
- Name the reviewer and the domain in which they are qualified.
- List the claim IDs or sections reviewed, not just the document title.
- Record evidence examined, tests run, exceptions accepted, and unresolved items.
- Set conditions for withdrawal of approval, such as a product release, regulation change, or failed monitoring check.
What provenance should I retain so another editor can reproduce the review?
Preserve enough context for an independent reviewer to repeat the retrieval and tests. Store the prompt or input version, model identifier when available, source URLs and versions, the claim-to-source map, test evidence, reviewer decisions, and unresolved-claim log.
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Keep generated drafts and corrected editions immutable or versioned. Record who changed a claim, why the change was made, and which evidence justified it. A machine-readable ledger is preferable for searching, expiry alerts, and audit export, while a human-readable report should explain important decisions and limitations.
Do not hide uncertainty in a footnote. If a source is inaccessible, conflicting, or too broad, mark the claim unresolved and either remove it, narrow it, or assign follow-up work. This is more defensible than retaining a confident sentence with a weak citation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How often should AI-written documentation be re-verified?
Set a documented interval and event-based triggers. Re-review whenever the model, authoritative source, requirement, regulation, dependency, product version, or operating context changes. The Intelligence Community framework asks how accuracy, bias, explainability, and drift will be checked over time; those questions apply to documentation as well as to the model that produced it.
| Trigger | Required response |
|---|---|
| New model or changed prompting pipeline | Regenerate or sample affected sections, compare claim status, and repeat high-risk tests. |
| Source revision, regulation, or standard update | Recheck every claim mapped to the changed source and update jurisdiction and version fields. |
| Product, API, dependency, or operating-system release | Re-execute commands and compatibility checks in the new environment. |
| Change in audience or operating context | Reassess scope, prerequisites, harm threshold, and acceptance criteria. |
| Monitoring failure, incident, or user report | Quarantine the affected instruction, investigate the evidence and test artifacts, then publish a corrected version or withdrawal notice. |
| Scheduled review date | Repeat source, citation, and representative execution checks even if no change is known. |
Use expiry rules for fast-changing material and longer intervals only where the underlying source and operating conditions are demonstrably stable. The interval should be justified in the provenance record, not chosen solely for convenience.
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How can I decide whether a verification process is strong enough?
Evaluate the process across seven dimensions rather than counting citations.
Best Value
- Evidence strength: Primary, current, scoped evidence outranks secondary summaries.
- Traceability: Atomic claim-to-passage mappings are stronger than paragraph-level links.
- Reproducibility: Another reviewer can repeat retrieval and tests from the retained inputs and environment.
- Reviewer effort: High-impact sections receive domain-expert sign-off and execution tests, not only a checklist scan.
- Risk coverage: The review addresses security, privacy, bias, stale versions, and realistic failure modes.
- Auditability: Versions, decisions, artifacts, and unresolved claims are retained.
- Freshness: Dates, expiry rules, and change-triggered reviews are explicit.
A document is ready to publish when its material claims have an evidence decision, its executable instructions have passed defined tests or been clearly qualified, high-impact sections have named human approval, and unresolved issues are either removed or visibly assigned. A polished draft without those records is still an unverified draft.
What should I check immediately before publication?
Use this final gate after the detailed review and before release.
- Confirm that the title, audience, jurisdiction, product version, and review date match the evidence.
- Check every number, quotation, requirement, and compatibility statement against a first-party source.
- Open each citation and verify entailment, qualifiers, exceptions, and scope.
- Run the documented commands or calculations in a clean, version-recorded environment where feasible.
- Review security, privacy, bias, and failure-mode checks for the document’s risk level.
- Obtain and record domain-qualified sign-off for consequential sections.
- Attach the prompt or input version, model identifier when available, source map, test artifacts, decisions, and unresolved-claim log.
- Set the next review date and list the events that require an earlier recheck.
Frequently Asked Questions
Can I publish AI-generated documentation if I cannot find a primary source?
Treat the claim as unresolved. Remove it, narrow it to what can be demonstrated, or assign a follow-up owner; do not use a secondary summary or the model’s confidence as a substitute for evidence.
How should I verify a quotation in AI-written documentation?
Find the original publication, confirm the wording character for character, and read enough surrounding context to preserve the speaker, date, scope, and qualifiers. Record the exact passage and edition in the claim ledger.
Can automated citation checks replace a subject-matter expert?
No. Automated checks can find missing links, run repeatable tests, and prioritize risk, but they cannot reliably judge legal, medical, safety, or security implications. A qualified reviewer must make and record the consequential decision.
What do I do when two authoritative sources disagree?
Record both passages, compare their dates, jurisdictions, versions, and authority, and state the conflict explicitly. Escalate to the responsible domain owner; do not silently select the wording that makes the AI draft easiest to keep.
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