Editorial and correction standards

How SQL Trail creates SQL content, verifies answers, reviews AI assistance, cites sources, and handles corrections.

SQL Trail publishes public learning pages under product-team attribution. Publisher, reviewer, review date, review status, AI assistance, and cited-source provenance stay in the manifest and structured data instead of a repetitive editorial card on 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.

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.

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.

A new guide is published only when it adds distinct learner value, useful links, and original examples or assets.

Overlapping guides are improved, merged, removed, or redirected instead of being left as thin duplicates.