trust
Editorial and correction standards
How SQL Trail creates SQL content, tests canonical answers, reviews AI assistance, cites sources, and handles corrections.

Editorial and correction standards overview
How SQL Trail creates SQL content, tests canonical answers, reviews AI assistance, cites sources, and handles corrections.
SQL Trail publishes public learning pages under product-team attribution. The visible editorial record names the publisher, reviewer, review date, review status, AI assistance, and cited sources for every indexable page.
Reviewer labels describe deterministic product checks rather than invented expert credentials. SQL Trail does not publish fake expert biographies, fabricated testimonials, fake user counts, or unsupported rich-result claims.
Review dates and correction log
The last-reviewed date changes only after a substantive technical or editorial review, such as changed PostgreSQL behavior, changed dataset semantics, changed validation examples, or a material correction.
Routine formatting, metadata tuning, and link maintenance do not refresh the last-reviewed date by themselves.
Material technical corrections are recorded in docs/content-correction-log.md with date, page or asset, issue, correction, reviewer, and whether the public review date changed.
After deployment, the visible correction route or configured contact method must point readers to correction handling; until that public channel exists, docs/content-correction-log.md is the internal source of truth and SQL Trail does not pretend an inbox exists.
Sources, AI assistance, and datasets
Dialect behavior, structured data, indexing, and externally verifiable claims cite authoritative sources in the page provenance record.
Meaningful AI assistance is disclosed where it helped draft public learning copy, generate original visual prompts, or organize review checklists; human and deterministic checks remain required before release.
Original datasets are fictional SQL Trail data worlds produced from deterministic seed scripts, metadata manifests, relationship diagrams, edge-case inventories, and visible SQL distribution assets rather than scraped or customer data.
Duplicate-content decisions
Automatically generated page variants are not published unless the brief shows distinct learner value, visible internal links, original examples or assets, and no dominant intent/topic collision.
Pages that become duplicative are improved, merged, removed from the indexable manifest, or redirected through the explicit redirect policy instead of being left as thin variants.