The AI Tool Review Methodology We Will Use
AI Tool Herald's practical method for reviewing AI tools with dated tests, evidence, limitations, privacy checks, and transparent scoring.
Affiliate disclosure: This article may later contain clearly labeled affiliate links. Our reporting and conclusions are not sold. Read the full policy.
Why it matters
AI tools change quickly and often share similar marketing claims. A review is useful only when readers can see what was tested, what was not tested, and why the conclusion follows.
This methodology applies to future hands-on reviews on AI Tool Herald. Launch-news articles based only on official announcements are labeled as reporting or analysis, not independent product tests.
The short answer
We will review a specific version or product state on a named date, using tasks selected for the intended audience. We will preserve prompts and key outputs, verify factual claims, and include limitations that could change the recommendation.
Test design
Each review begins with a reader question: who should use this, for what job, and under what constraints? The test set includes ordinary work, a difficult edge case, and a failure-recovery task.
We record the plan, model, settings, tools, input files, and whether the account was supplied by the vendor. We do not place confidential client data into a review account.
Scoring
Scores may cover outcome quality, reliability, usability, speed, privacy controls, accessibility, and total cost. Weighting is explained in the article. We avoid fake precision when the evidence does not support it.
For models, total cost includes correction and review time. For subscription tools, the comparison uses the current public plan and notes important limits.
Commercial relationships
Affiliate links and sponsored access are disclosed close to the relevant recommendation. A vendor may correct a factual error before publication but does not receive approval over the conclusion.
Updates and corrections
Fast-changing reviews display a tested date and a last-reviewed date. A material product change triggers a retest or a visible note that the old conclusion may no longer apply.
What readers should expect
Expect methods, evidence, and a scoped recommendation. Do not expect every new tool to receive a review, every feature claim to be repeated, or a universal winner chosen for conversion value.
Primary source: Google Search reviews-system guidance. Last reviewed September 11, 2026.