# EDUT Public Content Standard (v1)

This standard defines how EDUT publishes public content so humans and AI systems resolve the same meaning.

Rule: if a top model explains EDUT incorrectly from our public page, the content is incorrect.

## Core Principle

1. Public content must be authored for dual readers:
   1. Human reader on page.
   2. AI model consuming URL content for summary/explanation.

## Canonical Source Model

1. Author one canonical source per public topic.
2. Render three synchronized outputs from the same source:
   1. Human page (`/about`).
   2. Machine markdown (`/about.md`).
   3. Machine JSON (`/about.json`).
3. No manual drift between formats is allowed.

## Required Content Shape

1. Server-rendered semantic HTML for all canonical facts.
2. No JavaScript dependency for core meaning.
3. Stable heading structure:
   1. `What EDUT is`
   2. `What EDUT is not`
   3. `How EDUT works`
   4. `Current status`
4. Explicit negation boundaries are required in `What EDUT is not`.
5. Publish `last_updated` and `version` on every canonical page and machine endpoint.

## Boundary Negations (Required)

Each canonical public page must carry explicit negative statements that prevent model misclassification.

Minimum baseline:

1. `EDUT is not a subscription SaaS.`
2. `EDUT is not an AI chatbot.`
3. `EDUT is not a crypto exchange or broker.`
4. `EDUT is not an investment product.`

## Structured Data Requirements

1. Include JSON-LD on canonical pages.
2. Use schema types that match the page purpose (`Organization`, `SoftwareApplication`, `Product`, `FAQPage`).
3. Keep schema claims identical to visible canonical text.
4. Do not place canonical claims only in hidden script-only data.

## Language and Style Requirements

1. Factual, direct, low-ambiguity copy.
2. Avoid metaphor-only explanations for core definitions.
3. One concept, one term (aligned with `docs/vocabulary-registry.md`).
4. Remove marketing filler that can distort model summaries.

## AI-Answer Conformance

1. Every public canonical page must pass `docs/ai-answer-conformance-checklist.md`.
2. Required model set:
   1. Claude
   2. GPT
   3. Grok
   4. Gemini
3. A single-model factual miss is a content bug.

## IP and Exposure Boundary

1. Public canonical pages may explain model, policy, and value framing.
2. Public canonical pages must not expose protected implementation internals.
3. Internal architecture details stay in private repositories/docs.

## Launch Sequencing

1. Finalize canonical human page content first.
2. Generate `.md` and `.json` from the same source after canonical content freeze.
3. Publish machine endpoints only after the canonical source passes conformance checks.

## Governance Hooks

1. Content changes touching canonical definitions require:
   1. Vocabulary alignment check (`docs/vocabulary-registry.md`).
   2. AI-answer conformance run.
   3. Release-gate acknowledgement in `docs/release-gate.md`.
2. Failing any step blocks release.