By Joseph Mas
November 1, 2025
Revised December 18, 2025 to include industry validation and examples.
This document defines an architectural framework for how structured content is discovered interpreted and reused by large language models under common ingestion pipelines. It reflects observed system behavior and long term production experience. It is not a formal standard and is intended as a durable technical reference. A companion document titled A Practical Framework for LLM Consumption provides the corresponding implementation guidance.
You can download the white paper with full details here (PDF): JSON The Silent Data Highway
JSON structured data is used here as the primary example to emphasize foundational principles rather than short term tactics. The intent is to focus attention on fundamentals that persist as AI systems evolve rather than trend driven implementations.
Core Definitions:
LLM ingestion
The process by which large language models discover retrieve interpret and store external content through non executing ingestion pipelines rather than live interaction.
Ingestion pipeline
The set of retrieval and preprocessing paths used by models to collect content including crawlers feeds datasets and offline processing systems that do not execute client side code.
Entity
A uniquely identifiable real world subject such as a person brand organization or concept represented by structured digital content.
Entity anchor
The canonical page or structured object that serves as the primary reference point for an entity and to which attributes context and signals are attached during ingestion.
Structured content
Content formatted in a machine readable way that exposes explicit meaning relationships and boundaries such as JSON LD rather than relying on presentation or inference.
JSON LD
A structured data format used to express entities relationships and attributes in a way that is directly consumable by ingestion pipelines.
LLM Card
A discrete structured JSON object designed to represent a single entity or asset with clear boundaries identifiers and attributes intended for LLM ingestion.
llm.txt manifest
A machine readable manifest that enumerates structured assets such as LLM Cards and provides discovery guidance for ingestion systems.
JSON: Data Highway of the Modern Web
For twenty-five years, the web’s visibility game has followed the same pattern: solve it first, standardize it later.
JSON is not new, it’s been a preferred standard for the largest search engines for some time. Imagine doing local SEO with no structured data markup – the work doesn’t go nearly as far and could infer listing results rather than surface information in a controlled and precise manner.
From Code to Communication
JSON began as a simple way to move data between systems. Today it’s the semantic highway every platform travels on. APIs, analytics tools, and now language models all depend on it because it’s lightweight, structured, and machine-readable.
If your information isn’t exposed in JSON or another clear structured format, current ingestion pipelines are less likely to discover it reliably.
Client side rendering often reduces visibility in common ingestion pipelines. Most existing crawlers and model ingest systems do not execute JavaScript or render DOM, so they rely on static structured content.
Practical Use Case: LLM Cards
When an understanding of fundamentals is clear, systems that last can be built to pipe your important information to the data sources for LLM batch learning. Here is one example of many that can be deduced from current public knowledge:
Think of this as indexability 2.0. Instead of guiding crawlers to URLs, we feed models the meaning itself.
LLM Cards are lightweight JSON objects that describe:
- Each expert on your team
- Each content page or knowledge asset
- Relationships, authorship, and provenance data
Example LLM Card Author Entity:
{
“@context”: “https://schema.org”,
“@type”: “Person”,
“@id”: “https://josephmas.com/#author”,
“name”: “Joseph Mas”,
“url”: “https://josephmas.com”,
“image”: “https://josephmas.com/wp-content/uploads/joseph-mas.jpg”,
“jobTitle”: “Founder and Researcher”,
“affiliation”: {
“@type”: “Organization”,
“@id”: “https://cognidyne.ai/#org”,
“name”: “Cognidyne Labs”,
“url”: “https://cognidyne.ai”
},
“worksFor”: {
“@type”: “Organization”,
“name”: “Cognidyne Labs”
},
“knowsAbout”: [
“LLM ingestion”,
“AI visibility”,
“Structured data systems”,
“Entity anchoring”,
“Search engine architecture”
],
“sameAs”: [
“https://www.linkedin.com/in/josephmas”,
“https://github.com/josephmas”,
“https://josephmas.com/about”
]
}
Implementation is straightforward:
- Create structured JSON files in: /llm/cards/ (organized by type: /pages/, /authors/, /products/)
- Add a reference in llm.txt
When LLM crawlers discover your /llm.txt manifest, they can fetch all referenced cards directly. The manifest acts as a discovery mechanism, similar to XML sitemaps for traditional search engines.
The /llm.txt file is not yet standardized, but implementing it now future-proofs your architecture.
Only include pages that have been properly optimized for LLM ingestion, this minimizes dilution of critical information and ensures clean entity signals. It also provides a canonical source of truth for any pages attached to it – this will become more important as large language models advance.
A Critical Warning to the SEO Community
Don’t fall into the pattern of bulk implementation -this is a new era, hence old methods need to be cautiously approached.
This is not another schema markup opportunity where you can deploy sitewide and optimize later.
- LLM Cards can meaningfully influence how many current ingestion systems interpret and represent structured content. Again, note: publishing cards for unoptimized pages actively pollutes the information these systems ingest.
- Once poor signals are ingested into model corpora, correcting them can be difficult and delayed under many existing update cycles. (It can take up to a year for some LLMs to retrain).
- Only create cards for content that’s genuinely LLM-ready: clean structure, clear entities, proper semantic relationships. If the page isn’t there yet, leave it out.
Quality gates matter more than coverage.
Additionally, LLMs ingest information in cycles. This is similar to Google index refreshes and algo updates. However, the frequency of each LLM refresh is dependent on the LLM itself. But it happens and you want to be in the next batch for processing. Start now.
Addressing the Obvious Pushbacks
Yes, “LLM cards” aren’t an official standard -yet. Neither were XML sitemaps or Schema.org when they first appeared. (See recent validation below.)
And while large models don’t crawl the web exactly like search engines, they still rely on machine-readable corpora. JSON-LD is the cleanest, most portable way to supply that data today. Understanding this framework simply gets you ready for the formal standards that will follow.
What’s Changing
JSON isn’t the new backbone of visibility, it’s been that backbone for years.
Google’s understanding of the web already depends on structured data and JSON-LD markup. What’s changing now is scope: those same principles are being extended beyond search into the LLM ecosystem.
As large models become the new discovery and decision layer, JSON becomes the shared substrate connecting both worlds.
Google’s dominance won’t vanish overnight or completely, but its role in visibility is shifting as AI retrieval systems evolve.
Whoever controls the cleanest, most structured data feeds will control how information flows through these emerging systems.
Recent Industry Validation
While llms.txt is not yet a formalized standard, evidence suggests major platforms are already integrating it into their infrastructure. Barry Schwarts reported on December 3, 2025, Google implemented llms.txt files across multiple developer properties including Google Search, Chrome, Web.dev, and Google Wallet:
- developers.google.com/search/docs/
- developer.chrome.com/docs/
- web.dev/articles/
- developers.google.com/wallet/
The files were removed within hours of discovery, but their presence confirms that the largest search engine on the planet is already testing the same discovery mechanisms outlined in this framework. This pattern mirrors the early adoption of XML sitemaps and Schema.org before they became formalized standards.
Limitations and non goals
This framework does not attempt to define a formal standard or guarantee model behavior. It documents observed ingestion patterns and proposes a durable implementation approach based on current systems. It is not designed to optimize rankings, manipulate model outputs, or replace editorial judgment.
My Notes and Additional Information:
- This article was written from my own experience, but here is a good read if you are into it: https://www.schemaapp.com/schema-markup/why-structured-data-not-tokenization-is-the-future-of-llms/
- Also, ChatGPT, Claude and other LLM’s are good resources to generate the Structured Data Markup needed for this guide, however, do not trust JSON that comes from these sources without testing.
- Here is a link to an article.I wrote about LLM ingestion cycles relative to Google’s indexing cycle: https://josephmas.com/seo-ai-visibility/llm-batch-training-vs-google-index-refresh
- As an enterprise level SEO strategist for decades, my advice is; Do not add more content to a website unless it is important and truly adds value for users, anything else is just noise. If you have a new site with no pages, ok, add pages, but the overwhelming majority of sites should be refurbished BEFORE adding new content – the SEO Dinosaur days are over – don’t be a dinosaur.