Trust and quality

Editorial standards

IntuitiveAI.dev publishes free technical education. These standards explain how lessons are sourced, tested, maintained, and corrected without making claims that the available evidence cannot support.

Policy reviewed August 2026

How content is reviewed

Source-backed explanations

Technical claims should point to primary papers, standards, official documentation, or authoritative textbooks. References are attached to the lesson or book chapter they support.

Executable examples

Code examples are designed to be small, inspectable, and paired with expected behavior. Automated checks cover the curriculum data, manuscript structure, routes, and learning contracts.

Clear learning boundaries

Lessons distinguish intuition, implementation guidance, production trade-offs, and open questions. A completion certificate records course evidence; it is not an accredited qualification.

Corrections and updates

Learners can report an error from any page. Corrections are prioritized by severity, and technical pages are reviewed when underlying standards or implementation guidance changes.

What we publish

Direct explanations, interactive visual models, runnable practice, assessment evidence, projects, and chapter-based technical books. Public learning content is readable without a login.

What we do not claim

The material is educational, not professional legal, medical, safety, or financial advice. Examples simplify some production concerns and identify those boundaries where they matter.

Found something wrong?

Use the Feedback control on the affected page and include the claim, code, diagram, or source that needs review. Page context is included so the report can be reproduced.

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