How we evaluate technology
Our methodology is designed to make practical guidance more transparent, reproducible, and appropriately cautious.
Evaluation principles
We define the user problem first, document the test conditions, separate observed behavior from interpretation, and identify important limitations. For AI-related topics, we consider privacy, reliability, misuse risk, transparency, accessibility, and the user’s ability to verify results.
Evidence hierarchy
When possible, we prioritize official product documentation, standards, original research, public datasets, and direct testing. Secondary sources are used for context and cross-checking rather than as the sole basis for consequential claims.
Responsible conclusions
No score or recommendation should be treated as a guarantee. Users should review current documentation and apply their own judgment to high-impact decisions.