How YAML-in-HTML Turns Any CMS Into an AI Memory Engine

Prescott, Arizona / Syndication Cloud / August 1, 2025 / David Bynon

Key Takeaways:

• YAML-in-HTML embeds structured, trust-scored knowledge directly into web pages.
• Unlike SEO formats like Schema.org, it supports fragment-level memory that AI systems can evaluate independently.
• Works with CMS platforms like WordPress—no plugins or infrastructure changes required.
• Part of the Semantic Digest Protocol, it helps publishers make content retrievable, citeable, and trustworthy for AI.
• Structuring content at the fragment level ensures it exists meaningfully in AI memory.

Why the Web Fails AI Systems

The web was made for people, not machines. Humans interpret context. AI struggles to know what’s true, who said it, and where it came from.

Schema.org and JSON-LD improved SEO, not AI understanding. They operate at the page level, lack provenance, and don’t support claim-level retrieval. AI systems are left guessing.

“Since 2011, websites have used Schema.org and JSON-LD to feed SEO bots. But these formats were never designed for AI memory.” — David Bynon

YAML-in-HTML: A Bridge Between CMS and AI

YAML-in-HTML changes that. It embeds machine-readable, trust-scored memory inside standard HTML using inert tags. No JavaScript. No plugins.

What It Looks Like

data-visibility-fragment

data-type=”text/yaml”

data-sdt-class=”DataFragment”

data-entity=”plan:H5521-290-0″

data-digest=”2025-cms-ma-mapd-plan”

data-glossary-scope=”cms_landscape”

data-fragment-scope=”semantic-digest”>

Inside is YAML content—easy to read, easy to export, and fully retrievable by agents.

Inside a YAML-in-HTML Fragment

1. YAML Structure

Defines what the fragment is, what it belongs to, and how to classify it.

data-sdt-class: DataFragment

entity: plan:H5521-290-0

digest: 2025-cms-ma-mapd-plan

glossary_scope: cms_landscape

fragment_scope: semantic-digest

2. ProvenanceMeta (Trust Block)

Documents the source, license, and retrieval details.

ProvenanceMeta:

ID: 2025-cms-ma-landscape

Title: CMS MA Landscape File, 2025

Creator: Centers for Medicare & Medicaid Services (CMS)

License: Public Domain

Published: 2025-06-01

Retrieved: 2025-06-28

Digest: 2025-cms-ma-mapd-plan

Entity: plan:H5521-290-0

3. Semantic Data Atoms

The smallest unit of AI-retrievable knowledge.

Fields:

– id: in_primary

defined_term: Primary Care Visit

value: “$0”

unit: usd

confidence: high

derived: false

glossary: term-in_primary

source: 2025-cms-pbp

provenance_ref: “#provenance-meta”

Fragment Classes: Memory with Structure

Different fragment types serve different AI needs.

DataFragment

Stores raw, structured facts (costs, stats, values).

DefinedTermFragment

Machine-readable glossary entries with provenance.

entity: term:zero_premium

Term:

term_id: zero_premium

name: Zero Premium Plan

definition: A Medicare Advantage plan that has no monthly premium beyond Part B.

FAQFragment

Question-and-answer pairs for retrieval without hallucination.

FAQ:

question: Are zero-premium Medicare Advantage plans available in all counties?

answer: No. Availability varies by county.

IndexFragment

Directories of entities (e.g., list of Medicare plans).

MetaFragment

High-level metadata about entire datasets.

Implementation: No Plugins Required

WordPress

Just add the block and YAML into a post or page.

• Fully inert and DOM-safe
• Invisible to humans, readable by crawlers

Static Sites (Jekyll, Hugo, Eleventy)

Embed YAML-in-HTML fragments as partials or template components.

Enterprise CMS

Define custom fields or server-side generators that output valid YAML-in-HTML. Key requirement: preserve the and YAML formatting.

Real-World Applications

Healthcare

• Plan comparisons, provider data, medical definitions
• Improves AI-generated health answers with facts

Finance

• Investment details, disclaimers, risk profiles
• Helps AI avoid outdated or incorrect financial info

Education

• Definitions, curriculum alignment, statistics
• Strengthens tutoring systems and reduces misinformation

Legal

• Statutes, citations, jurisdiction-specific rules
• Enables AI to cite exact legal text, not paraphrased guesses

Compatible with Modern AI Systems

• Model Context Protocol (MCP): Fragments can be queried by agents
• LLMs: YAML is ingestible directly or via conversion (e.g., JSON-LD)
• RAG: Fragment-level memory units with built-in trust
• Assistants/Agents: Clear citation and grounding from source fragments

“If MCP is the USB-C socket, YAML-in-HTML is the micro thumb drive. It’s small, lightweight, and universally pluggable.” — David Bynon

A New Paradigm: Memory-First Publishing

Traditional Web:

• Optimized for crawlers
• Page-level markup
• Success = search rankings

Memory-First Web:

• Optimized for AI retrieval
• Fragment-level trust
• Success = accurate citation in AI answers

YAML-in-HTML brings this future within reach—no new frameworks, no vendor lock-in. It runs on the web we already have.

By adding these machine-readable fragments alongside human-readable content, publishers can serve both audiences effectively—ensuring their expertise is accurately represented in both human research and AI-assisted information retrieval. Learn more in this USA Today story.

For an in-depth overview of this methodology, see the original announcement on Medium.com (https://medium.com/@trust_publishing/the-web-just-got-a-memory-introducing-yaml-in-html-1491e5d2c8fb).

To learn more about implementing YAML-in-HTML and the Semantic Digest Protocol in your own projects, check out David Bynon’s documentation at SemanticDigest.org.

 

 

 

David Bynon

101 W Goodwin St # 2487
Prescott
Arizona
86303
United States

Originally Appeared Here