Evidence before output
Our process is designed around primary search-engine documentation, reproducible checks, crawl and analytics evidence, and clearly attributed industry research. We do not present a copied archive page, a merchant description, or an AI-generated draft as completed research.
- Define the decision. We start with the problem a reader must solve, not a keyword variation.
- Check primary documentation. Search-engine and platform documentation outrank commentary when a factual rule is available.
- Validate the workflow. Recommendations are tested against observable site behavior, tool output, or a reproducible example where possible.
- Separate fact from judgment. Confirmed requirements, working hypotheses, and editorial recommendations are labelled by context.
- Review after meaningful changes. Pages are revisited when platforms, standards, or search behavior materially change.
Research, experience, and opinion
We distinguish direct observation from second-hand information. When we have not personally used or tested something, we say so through the wording and production notes. Editorial judgment is explained with criteria instead of being disguised as an objective fact.
Use of AI and automation
Automation may assist with source organization, transcription, structured comparisons, grammar, or an initial draft. It may not publish autonomously. A human editor is responsible for checking claims, links, dates, relevance, originality, and whether the page adds value beyond its sources. Materially AI-assisted pages are marked in their production notes.
Updates
Pages are reviewed when source facts change, a reader reports a material issue, or the topic becomes stale. The visible updated date must reflect a meaningful change to the main content, evidence, structured data, or useful links rather than an automatic date refresh.