1. Scope the decision
Every review begins with a job to be done and a defined reader. A universal 'best tool' claim is avoided when the answer depends on budget, data sensitivity, ecosystem, technical ability, or team size.
Editorial methodology
Verification before verdict.
A review should make its evidence, limits, freshness, and commercial context visible. This methodology defines how AI Tools Guide separates current research from preserved archive material.
2. Check primary sources
Product identity, release claims, plan names, and availability are checked against provider documentation or announcements where possible. Secondary sources may add context, but they should not replace a primary source for a provider claim.
3. Record the test
When hands-on testing is performed, the task, date, plan, device, prompts, and observed limitations should be recorded. Archive claims without a surviving test record remain labeled as legacy research rather than being promoted as verified 2026 findings.
4. Separate facts from judgment
A provider-stated feature is not the same as an observed result. Product facts, test observations, and editorial interpretation should be presented as distinct layers.
5. Recheck and correct
Fast-changing details receive freshness labels. Material errors are corrected, and significant changes should be recorded in the correction log rather than silently overwritten.