CookMyRank Editorial & Corrections Policy

How we create and review content

Every factual claim must be supported by documented business context, a visible first-party methodology, or a relevant primary source. Drafts are checked for unsupported statistics, invented research, chronology conflicts, duplicated search intent, and missing source attribution before publication.

We prioritize direct answers, practical examples, definitions, formulas, limitations, and clearly labeled hypothetical scenarios. We do not publish precise performance claims simply because they make an article sound authoritative.

• First-party studies or internal data require a public methodology, sample size, dates, platforms, prompts, and measurement process.
• Statistics and research claims must link directly to the source beside the claim they support.
• Examples are labeled as hypothetical unless they come from documented evidence.
• Drafts must pass an evidence gate before they can be published.

Sources, authorship, and structured data

We prefer official documentation, standards bodies, and original research over secondary summaries. Each research-led article includes a Sources and methodology section, while its structured data reflects only entities and FAQs that are visibly discussed on the page.

Articles published under The CookMyRank Team are organizational work produced through this editorial process. We do not represent a team byline as a fictional person or invent personal credentials.

Corrections and updates

When we identify a material error, unsupported claim, broken citation, or outdated instruction, we correct the page and update its modification timestamp. If a claim cannot be verified, we remove it rather than soften or repeat it.

Readers can report a possible error to support@cookmyrank.com. Please include the article URL, the passage in question, and any primary source that helps us verify the correction.

Independence and limitations

CookMyRank content may discuss features offered by CookMyRank, but product references do not override the evidence standard. We distinguish measured results from illustrative examples and avoid guarantees about rankings, citations, or AI recommendations.

AI answers vary by model, version, prompt wording, location, personalization, and time. Monitoring guidance therefore emphasizes fixed prompt sets, repeated runs, documented conditions, and trends rather than a single response.