A useful AI-tool review should let another editor understand where each important statement came from and what the evidence does—and does not—prove. A link list alone is not an evidence record: readers need to see which claim a source supports, when it was checked, which product surface or version it describes and whether the publisher independently tested anything. This guide provides a lightweight documentation workflow for reviews, comparisons, Best Lists and practical guides.
1. Start with a claim register
Before drafting, split the proposed article into checkable claims. “The tool is useful” is an editorial conclusion; “the product offers a transcript editor” is a product-feature claim; “it is more accurate” is a comparative performance claim. Give each claim a record containing the exact wording, reader impact, evidence type, source URL, checked date, product or plan scope, rollout or region caveat, and the next review date.
Label evidence instead of blending it. Useful labels include: vendor documentation, vendor announcement, direct observation, reproducible test, third-party study and editorial inference. A vendor’s benchmark remains a vendor-reported result. It does not become an independent test because the article links to it. If the team did not run a test, state that plainly and do not invent sample sizes, scores, screenshots or user experience.
2. Match the evidence to the claim
Use the most direct source available. A product page can establish that a named feature is advertised; a help page or technical document can explain how the feature is intended to work; a pricing page is needed for current plan details; a policy page is needed for data-handling claims. A broad newsroom page may confirm an announcement but may not answer questions about account eligibility, limits, geographic availability or implementation.
Open the cited source and capture the relevant section or a short paraphrase with the exact heading. Check whether the document distinguishes preview from general availability, consumer from business plans, web app from API, or administrator-controlled settings from defaults. Keep the source’s caveats near the claim. If a source is silent, do not infer an answer from marketing language or from another product surface.
Google’s people-first content guidance encourages useful original analysis, clear sourcing and an honest explanation of how content was produced. NIST’s AI Risk Management Framework is a voluntary framework for organizing AI risk work; it is not a certification for a product or a substitute for domain review. These sources inform our documentation method, not a claim that a review has been independently validated.
3. Record tests without overstating them
If you do run a test, save the prompt or task, input files where sharing is permitted, model label shown in the interface, settings, date, number of attempts, acceptance criteria, outputs, corrections and known failures. Separate setup from result. A single successful response can illustrate a workflow, but it cannot establish a general accuracy rate or prove superiority.
Make comparisons reproducible: give each candidate the same task, equivalent inputs and a defined scoring rubric. Note any unavoidable differences such as distinct integrations, plan access or output formats. If conditions were not matched, describe the comparison as an editorial feature review rather than a benchmark. Readers should be able to see what was observed, what came from official documentation and what remains unknown.
4. Use a stop rule and maintain the record
Mark a claim “supported,” “partially supported,” “outdated” or “unverified.” Publish only wording that matches the evidence. If a major decision claim is unverified, remove it or send it for specialist review; a disclaimer does not repair an unsupported conclusion. For a time-sensitive fact such as a model name, access limit or price, set a review date and recheck the original source before the next update.
| Record field | What to capture | Editorial use |
|---|---|---|
| Claim | One factual statement or judgment | Prevents a source from appearing to support unrelated points |
| Source and scope | Official URL, page heading, date, plan, model and region | Shows what the source actually covers |
| Evidence type | Vendor statement, test, study or editorial inference | Prevents vendor claims being framed as independent results |
| Limit and owner | Uncertainty, reviewer and next-check date | Creates a clear follow-up rather than false certainty |
Our editorial judgment is that traceability is more valuable than decorative citation volume: a small number of relevant sources connected to precise claims is stronger than a long list of unrelated links. This method is for editors and readers evaluating AI-tool information. It is not a certification, legal opinion or claim that AI output is accurate. For the next step, see the output verification checklist, tool-selection guide, and privacy review guide.
Best fit: editors documenting claims, evidence and uncertainty before publishing a product review. Not a fit: teams seeking a formal audit or certification from this evidence log alone. Limitations: source quality varies, vendor documentation describes the vendor’s position, and this workflow cannot prove a feature performs as claimed. For our site-wide method, see the AI-generated content audit guide and the public How We Test policy.