Editorial Policy
Last updated:
What we publish and why
The ProductOS blog covers AI product development: writing PRDs with AI, going from idea to shipped app, AI app builders, and AI workflows for product managers. We publish to be genuinely useful to people doing this work, not to chase keywords. Every article maps to a question we have actually been asked or a problem we have actually solved building ProductOS.
How content is researched
Claims are grounded in primary sources: our own product data and testing, official documentation of the tools we discuss, and original studies. When we cite a statistic, we link to where it originally appeared, not to a blog that mentioned it. Comparison content is based on publicly available capabilities and pricing at the date shown on the page, and we note that date.
Who writes and reviews
Every article carries a byline of a real, named member of the ProductOS team, with a public profile you can verify. See our author pages. Technical claims are reviewed by the team member closest to that part of the product before publishing. We do not publish under invented personas or anonymous staff bylines.
How we use AI
We build an AI product, and we use AI in our writing workflow, for research synthesis, outlines, and first drafts. A named human author always reviews every draft, verifies the claims, adds first-hand experience and screenshots from real usage, and takes authorship and responsibility for the published piece. AI is a drafting tool here, not an author.
Updates and corrections
Articles show a visible published date, and a "last updated" date when the content has materially changed. We refresh competitive and pricing content quarterly, since this space moves fast. If we get something wrong, we correct the article and note the correction. Spotted an error? Email support@productos.dev or use our contact page.
Honesty in comparisons
Our comparison pages cover competitors' genuine strengths, not just ours. We would rather you pick the right tool, even when it is not ProductOS, than feel misled. Where we are the better fit, we say why with specifics; where we are not, we say that too.