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Editorial Principles
ProdAI Stack exists to help engineers build more reliable production AI systems. Our standards center on technical accuracy, practical usefulness, transparency, and reproducibility.
- accurate and technically specific
- practical and evidence-driven
- vendor-aware but not vendor-dependent
- clear about limitations
- useful in real engineering environments
Sources
We prefer authoritative sources for factual and implementation-specific claims: official documentation, standards, specifications, engineering documentation, primary research, respected engineering publications, original implementation evidence, and reproducible experiments.
Community discussion can reveal real-world problems, but opinion alone is not authoritative evidence.
Technical Claims
Important claims should be supported by official documentation, source code, reproducible implementation, automated tests, experiments, or clearly explained engineering reasoning. When evidence is limited, we should say so.
Code Examples
Code should illustrate the engineering concept clearly. Verified reference implementations may be checked with type checking, linting, automated tests, deterministic harnesses, fault injection, and example execution.
An article snippet may be simplified; the related Lab is the more complete implementation where one exists.
Experiments and Measurements
Measurements must include enough context to understand what was measured. We distinguish local deterministic experiments, mocks, simulated timing, synthetic metrics, fault injection, external-provider measurements, and real production observations.
We do not present synthetic test-harness results as external-provider benchmarks.
Architecture Diagrams
Diagrams explain boundaries, relationships, and decisions. They may simplify implementation details and should be used as design guidance rather than literal deployment specifications.
Review
Before publication, content may be reviewed for factual consistency, source quality, code correctness, experiment interpretation, architecture consistency, SEO, readability, internal linking, and media accuracy.
The depth of review depends on the content.
Updates
Articles may be updated when APIs or libraries change, guidance becomes outdated, new evidence appears, code changes, or important reader feedback is received. Updated and technical-verification dates are shown separately where available.
Corrections
Material errors should be corrected transparently. See our Corrections Policy.
Editorial Independence
Recommendations should be based on engineering merit. Sponsorships, paid placements, affiliate relationships, or commercial partnerships must be clearly disclosed.
Coverage of a vendor does not imply endorsement.
AI Assistance
AI tools may assist research organization, drafting, coding, testing, editing, or media preparation. AI output is not authoritative evidence without appropriate verification. Read our AI Assistance Policy.
Reader Feedback
Useful corrections, reproducible counterexamples, updated documentation, and better implementation evidence help improve the publication.