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Standards → AI models

AI model standards

AI is woven into WBP's architecture — Content Generation Engine, Knowledge Base retrieval, Planner, EEAT Engine, Quality scoring — not bolted on. That requires responsible governance. Every AI model WBP integrates with follows two non-negotiable principles: no silent AI use, and no default trust in AI output.
TL;DR: Two principles govern every model integration. Principle 1: explicit consent — no AI runs without BYOK or Managed AI opt-in. Principle 2: AI output is not trusted by default — it runs through Quality → Accuracy → E-E-A-T checks before publish.

1. The two WBP AI principles

Principle 1 — Explicit consent required. No WBP feature calls an AI model without you opting in explicitly. Two integration modes: BYOK (bring your own API key, calls go directly from your server to the provider) and Managed AI Service (calls route through WBP's server on a paid plan). Both require a deliberate action. There is no silent AI in a default install.

Principle 2 — AI output is not trusted by default. Every AI-generated field runs through a four-stage validation pipeline before it's eligible to publish: Quality scan → Accuracy check against your WBP Knowledge Base → E-E-A-T evaluation → optional human review. AI convenience never lowers the quality bar.

2. The four models we publish for

ChatGPT (OpenAI)

Most widely used across WBP. GPT-4o-mini default, GPT-4o for depth, o1/o3-mini for reasoning. Powers Content Generation, Auto-Tagging, Planner, Quality scoring.

Claude (Anthropic)

Constitutional AI training. Lower hallucination, strong refusal behaviour. Used for technical / YMYL content and quality assessment.

Gemini (Google)

Google's AI for Google's search — a transparency-first tension. Same quality gates as any other model, no ranking-advantage claims.

Disclosure best practices

Four disclosure formats built in: LLM Summary Box widget, automatic disclosure text, AI Use Policy page, schema aiGenerated markup.

3. AI output validation pipeline

  1. 1

    Quality scan

    AI output is scored on info density, comprehensiveness, unique value contribution, and SEO-first-content detection. Failing the scan kills the row.

  2. 2

    Accuracy check

    Claims are cross-checked against your WBP Knowledge Base (your first-hand sources, docs, product specs). Contradictions are surfaced before publish.

  3. 3

    E-E-A-T evaluation

    EEAT Engine grades the output: is there evidence of experience, expertise, authoritativeness, trust. Missing signals block the row.

  4. 4

    Optional human review

    For YMYL, high-risk topics, or when the pipeline flags uncertainty, the row queues for human review before publishing.

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