EEAT Code

TL;DR: If your site isn’t structured, sourced, and repeatable, Google — and every other AI system — will ignore you, paraphrase you, or worse: absorb you without attribution.

🚪 The Door Is Closed to Most Publishers

For years, publishers believed they were building trust by creating “great content.”

They hired credentialed authors. Bought stock photos. Added breadcrumbs.
Some even slapped on Schema markup and called it a day.

And yet, Google doesn’t reward most of them. Not with snippets. Not with rankings.
And definitely not in AI Overviews.

That’s because trust, in the modern Google ecosystem, isn’t about your brand name, your content team, or even your backlinks.

Trust is structural. It’s pattern-based. And it’s modeled.

🧠 What Google Actually Trusts

Google isn’t a human reader.

It’s an AI system parsing billions of pages a day, looking for machine-readable patterns it can predict, interpret, and re-use safely.

So when it encounters a new publisher — or even a legacy one that suddenly changes its format — it doesn’t ask:

“Is this writer qualified?”

It asks:

“Can I model this content and reuse it without error?”
And if the answer is yes, you get elevation.

If the answer is no, Google falls back on legacy trust — domains it already knows, like WebMD or Healthline. Even if your content is better.

I know this because I have experienced it. And I’m still living in this reality with competing sites that have legacy trust.

So I test. Poke. Prod. And I put in the time to understand what Google and large language models really want.

🔍 The Trust Hierarchy in Practice

Google operates on three levels of trust:

1. Legacy Trust

Big brands with long histories get automatic leniency. Google already has confidence scores baked into its system.

2. Pattern Trust

Sites that demonstrate structured consistency, clear labeling, and stable formatting earn machine trust — even without big-brand clout.

3. Model Trust

When Google can accurately extract and represent your data in an AI Overview, rich snippet, or answer panel, it means you’ve crossed the line into true machine-verifiable trust.

💡 Case in Point: Medicare Plan Pages

On Medicare.org, I publish thousands of structured Medicare Advantage plan pages.

Each one includes:

• Clean field-value formatting (MOOP, Premium, Star Rating, Deductible)
• Public citations linked to trusted, government datasets
• Uniform headings, layouts, and visual patterns
• Dataset Schema grounded in real source files

No gimmicks. No tricks. No “fluff content.”
Just a clear system — at scale.

And the result?

Google began rendering my content as its own structured plan answer cards — with no API, no JSON feed, and no special access.

I didn’t submit anything.

I just trained the crawler — with structure.

🔬 The Schema Illusion

Here’s the trap: many SEOs believe Schema markup is the key to winning trust.

But Schema without structure is noise.

• If your content isn’t clean, Schema doesn’t help.
• If your layout isn’t repeatable, Schema gets ignored.
• If your facts aren’t backed by citations, Schema doesn’t increase trust — it invites scrutiny.

You can’t “markup” your way into trust.

You have to build for AI. And that means structure first, Schema second.

🤖 What Machines Are Actually Doing

Google isn’t alone anymore. OpenAI, Meta, Perplexity, and Anthropic are all racing to become the next universal answer engine.

And they’re all doing the same thing:

Crawling, parsing, embedding, and modeling your content — not just indexing it.

Here’s the shared logic across AI search and LLM systems:

1. They crawl your page (or get it via Bing’s API or Common Crawl)
2. They extract text and structural layout
3. They build a content embedding — a numerical vector representing meaning, format, and style
4. They compare that vector against the question prompt
5. If your content is:
   • Structured
   • Verifiable
   • Consistent

…they’ll use it to generate answers. Sometimes directly. Sometimes paraphrased. Sometimes with no attribution at all.

So even if Google loses its grip on search, the future will still belong to the publishers who trained the machines what clean, trustworthy data looks like.

Schema helps, but structure is the substrate.
That’s what models learn from — and reuse without asking permission.

🔥 What “Helpful Content” Really Means (To Google)

Let’s be honest. Most publishers think “Helpful Content” means:

• Answer the user’s question.
• Be original.
• Don’t write for search engines.

But that’s not how Google evaluates helpfulness.

In reality, helpful content is content Google can reuse confidently.

That means:

• Consistent formatting
• Verifiable data
• Embedded citations
• Structural clarity
• Low ambiguity
• And predictable output at scale

This is exactly why vague blog posts, generic reviews, and over-optimized affiliate pages lost visibility — not because the writing was bad, but because the machine couldn’t trust what to do with it.

Helpful Content isn’t just a content quality filter.
It’s a structure and trust model filter.

If Google can’t model your page and reuse your output in an AI Overview, snippet, or knowledge panel… your “helpful” content is invisible.

🎯 What You Should Do (If You Want to Win AI Search)

1. Structure everything — repeatable patterns, clean headings, field-label/value pairs
2. Back every key fact with a source — ideally a public one
3. Use Schema only to reinforce clarity — not to replace it
4. Scale with consistency — the machine must see enough volume to trust the pattern
5. Avoid junk signals — minimize fluff content, ads, and visual noise that breaks predictability

🧠 Final Takeaway

Google may be the first to reward structured trust.

But it won’t be the last.

AI doesn’t care who you are.
It only cares what it can model — cleanly, safely, and at scale.

If your content isn’t structured, sourced, and repeatable, the next generation of answer engines won’t cite you.

They’ll absorb you.

You’re not publishing for humans anymore. You’re publishing for machines — and machines don’t trust people. They trust patterns.

Written by David Bynon
Publisher of EEAT.me, creator of TrustTags™, and architect of multiple structured content systems powering Medicare and health publishing platforms.

 

Most publishers wait for Google to figure them out.
I trained it.

Not with a sitemap. Not with a feed.
But with structure, trust, and clarity — at scale.

📊 What Happened

I published about 1,500 Medicare Special Needs Plan pages.
Each one followed the same strict layout:

• Consistent plan identifiers (e.g., H5859-001-0)
• Tabular or definition list formatting (Premium, Deductible, MOOP)
• CMS-based citations embedded on every page
• JSON-LD Schema combining WebPage, Dataset, Product, and HealthInsurancePlan — no fluff

Then something happened.

I searched a plan ID like H5859-001 costs.

And instead of a normal snippet or AI Overview, Google responded with a structured answer card — using my phrasing, my structure, and my data.

No one else’s.

🧠 Here’s the Twist

This wasn’t powered by:

• A data feed
• An API
• Microdata
• Review markup

It wasn’t even a proper Knowledge Panel or AI Overview.

It was something new:

A programmatically generated card, based entirely on how well my content was structured — and how much Google trusted me.

No scraping. No schema games.
Just clean design + strong data provenance + scaled repetition.

🏗️ How I Trained Google (Without Even Trying)

Let’s be real. I didn’t beg Google for visibility.

I built the exact content system its AI crawlers need:

• Every field labeled the same way, across 5,000+ plan pages
• Every plan grounded in CMS Dataset Schema with human-readable and machine-readable provenance
• Every page consistent in layout, headings, and labeling
• No distractions. No fluff. Just facts.

Over time, Google started to recognize that:

• These fields are reliable
• These values are clean
• This site can be trusted to represent Medicare data visually

And it learned.

🚀 Structured Trust at Work

Google rendered:

• Plan year
• Premium
• Deductible
• MOOP
• My exact phrasing from the site

All without ever requesting the JSON behind the curtain.

Because the trust was already there.

🧠 The Lesson

If you want Google to understand your content, don’t just optimize for SEO.
Train it.

Show it the same patterns.
Ground every fact.
Scale structure, not speculation.

And one day, it stops guessing.

It starts copying you.

 

Most sites just list Medicare plans.
No context. No structure. No trust.

I went the other way.

⭐ The Only Site with Real, Structured Ratings

I built a system to publish structured AggregateRating data for thousands of Medicare Advantage and SNP plans across all counties.

• Ratings sourced directly from CMS star data.
• Structured using Schema.org Product and AggregateRating.
• Reinforced with Dataset Schema and visible citations.
• Attributed to a real publisher (Organization) to ground authorship.

Every plan page had:

• reviewCount, ratingValue, and bestRating
• CMS citations with Dataset IDs
• Human-readable references to match the Schema
• JSON-LD so clean it could pass a code review at Google

This wasn’t Schema spam.
This was structured trust at scale.

🟡 Then Google Blinked

Right after I rolled out the final batch — nearly 4,500 Medicare Advantage plan pages with rating stars — Google pulled the rich snippets.

All of them.

No errors. No penalties. Just… gone.

Then they came back.
Then they vanished again.

Had I gone too far?

No. I’d just reached a point where Google had to stop and ask:

“Is this guy legit… or just really good at markup?”

🧠 What Really Happened

Google does this in every sensitive vertical.
When they see perfect Schema rolled out at massive scale — and nobody else is doing it — they initiate a trust review.

Think of it like a red-light reflex in the algorithm:

• Pause the visual reward (stars).
• Reevaluate source trust, markup consistency, and user signals.
• Watch what happens.

I stayed the course.
Didn’t flinch.
Didn’t make excuses.
Didn’t blame Schema.

And guess what?

⭐ 24 Hours Later — The Stars Came Back

Google finished its audit.
The system re-evaluated the pages.
The trust signals held.

Stars are back — on all 4,500+ plan pages.

Why?
Because the data is clean. The Schema is real. The citations are public.
And the trust isn’t a gimmick — it’s a system.

💡 Final Takeaway:

When you’re the only publisher doing something right at scale, Google will test you.
But if your trust is real and your data is solid, you pass.

Not with a manual review.
Not with a plea.

With stars.

What happens when you structure your content for trust, without tagging it?
Google finds it. Google understands it. And in my case — Google used it.

Earlier this week, I added FAQ content to a new batch of Medicare plan pages on Medicare.org. These FAQs were clean, consistent, and fact-rich — but I intentionally left out the FAQPage Schema.

Why? Because I wanted to test what would happen when Google encountered helpful content without trying to convince it that it’s helpful.

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The Result: 10-for-10 in AI Overviews

Within 48 hours of those plan pages being indexed in Search Console, I searched queries like:

“What are some FAQs about plan H7849-129?”

On all 10 pages, Google returned an AI Overview featuring answers directly from my new FAQ content — with no Schema applied.

I even switched up my search query to see how Google responded. The results were similar:

Google’s AI generated response:

• Mirrored my exact phrasing and data points
• Referenced key plan details like premiums, copays, and MOOP
• Reflected my templated TrustBlock FAQ formatting

—

But Here’s What’s Even More Interesting…

Google hasn’t yet used that same FAQ content in People Also Ask (PAA) results.
But it is using similar FAQ-style content from competitors — who also have no Schema.

Why does that matter? Because it proves something most SEOs never say out loud:

Trust is a separate calculation — and it governs what surfaces where.

—

How Google Decides What to Trust

PAA isn’t just about keyword targeting or formatting. It’s a curated answer box.
Google tends to pull from:

• Older, high-trust pages
• Content that’s been crawled and engaged over time
• Sources with proven user alignment

That’s why Google is generating AI Overviews using my content…
but waiting to feature that same content in curated PAA boxes.

It’s not a formatting issue. It’s not a Schema issue.
It’s a trust issue — and Google’s still watching.

—

What This Proves

• You don’t need Schema to get included in AI responses
• Well-formatted FAQ content is semantically readable by Google
• Schema should confirm trust — not try to manufacture it
• Google’s AI uses content it believes is provable — even before it’s promoted to answer boxes

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My Strategy: Structure First, Schema Second

This was an intentional test — and it worked:

• I added structured FAQ content with high-value answers
• I withheld Schema to see what Google would do
• I observed AI Overview inclusion without markup
• And I saw PAA being withheld, likely pending trust maturity

The next step? Start layering in FAQPage Schema only on proven pages — using Google’s behavior as a signal for when to scale trust declarations.

This is what I mean when I say:
Structure first. Schema second. Earn trust.

— David W. Bynon

You don’t build trust with a paragraph that says, “In my experience…”
You build it with content that proves itself — clearly, repeatedly, and at scale.

Lessons Learned

After Google’s March 2024 Helpful Content update, I felt the sting of defeat. My site — compliant, accurate, and deeply built — took a hit while competitors with newer, thinner content kept ranking.

I couldn’t understand why. So I went deeper — not into keywords, but into how Google thinks.

And what I realized was this:
Absent truly helpful content, Google falls back on one thing — domain authority.
That’s all it has left when structure, citations, and trust signals aren’t there.

So I stopped listening to what the so-called experts had to say about EEAT… and started building and testing systems.

I created TrustBlocks™ — modular, structured content units designed to be:

• Helpful to users
• Understandable by machines
• Backed by facts, citations, and Schema

Each TrustBlock is engineered for clarity, consistency, and extractability — not just for SEO, but for voice search, AI Overviews, and regulatory-grade transparency.

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TrustBlock #1: The Hero Section

This block appears at the top of every plan page on Medicare.org. It includes:

• Plan Name
• Plan ID (e.g., H8003-007-0)
• CMS Star Rating (with year)
• Enrollment Count (a soft signal of popularity)

This TrustBlock creates an instant trust signature. It answers key user questions and provides Google with clearly structured, crawlable entities that can feed directly into Knowledge Panels, snippets, and AI responses.

—

TrustBlock #2: Plan Costs in Context

The second TrustBlock is where most competitors stop — they paste data into tables. I go further.

I narrate the data in rich, natural-language paragraphs:

• “Primary care visits have a $0 copay…”
• “Out-of-network ambulance transportation is $275…”
• “Your annual MOOP is $5,900 for in-network care…”

These paragraphs are built from CMS data, matched to human-readable citations, and aligned with Dataset Schema. They’re digestible for users and indexable for Google’s AI. And they give me what I call extracted trust.

Here’s an example:

It’s the difference between formatting facts… and proving them.

—

TrustBlock #3: The FAQ Section

Most FAQs are fluff — mine aren’t.

Each FAQ is:

• Based on real Medicare queries
• Templated for consistency, but varied in format and tone
• Packed with facts
• Written without Schema initially — to let Google test the content on its own

Only after proving Google is using the content in AI Overviews or snippets do I layer in FAQPage Schema — giving the block even more weight.

—

Why TrustBlocks Work

Each block is engineered for:

• Semantic clarity – readable, labelable, reusable
• Data-backed trust – built on CMS facts and Dataset Schema
• Scalable structure – all blocks are templated, but smart and varied
• Multi-format readiness – usable in AI Overviews, voice search, feed distribution, and PR

Together, the three TrustBlocks create a full trust layer across the page — one that Google, users, and AI systems can interact with confidently.

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TrustBlocks = Built Experience

I don’t need to say “in my experience helping people with Medicare…”
The data does the explaining.

Each TrustBlock is a proof object — not just a content module.
It demonstrates experience, shows expertise, and reflects authoritativeness through structure, precision, and clarity.

That’s what EEAT actually looks like when it’s done right — one block at a time.

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TrustBlocks for Articles

While my initial TrustBlock framework focused on Medicare plan pages, the same modular trust strategy applies to long-form editorial content — especially in regulated or sensitive verticals.

That’s why I’ve integrated the TrustBlocks concept directly into my TrustStacker™ plugin — giving me the ability to inject helpful, verifiable content blocks into any article using shortcodes and AI-assisted generation.

These article-level TrustBlocks include:

• – Curated, fact-based claims linked to canonical sources
• – Structured glossary terms using Schema.org DefinedTerm
• [keytakeaways] – Summary blocks generated from the article content
• [trustfaqs] – Smart FAQ pairs aligned to the topic (with or without Schema)
• [trusthowto] – Step-by-step process blocks for procedural content
• [trustcitations] – Human-readable source attribution with Schema-backed provenance
• [trustspeakable] – Voice-optimized excerpts ready for Google’s Speakable schema

Many of these blocks are AI-assisted, built from the canonical source content on the page, then enhanced with curated structure, definitions, or citation overlays.

This isn’t AI-generated crap. I’m using AI to help distill original content into helpful blocks that both humans and machines can consume.

The result? Articles that don’t just say something useful — they prove it. Structurally. Transparently. At scale.

— David W. Bynon

Most Schema case studies show off what worked. This one shows what didn’t — and why that matters more than people think.

Here’s what happened when I deployed Dataset Schema to MedicareWire.com — without fully aligning it with visible citations and structured content blocks:

What you’re seeing here is a correction.
An early spike in valid datasets… followed by a sharp decline, a rising stack of invalid items, and a steady loss of Schema trust.

Why am I showing this?

It comes down to this one fact. You can’t learn what works until you discover what doesn’t work. So I have been using my MedicareWire site as a punching bag.

To be fair, Google relegated MedicareWire to the trash heap anyway. It couldn’t fall much lower. So, using it as a test platform makes perfect sense. And it has been teaching me a lot.

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The Mistake: Schema Without Trust

The initial Dataset Schema was technically valid. It passed markup checks and included CMS.gov source URLs.
But it lacked supporting structure in the page body.

Specifically, it had:

• No linking of data points to their sources
• No human-readable citations visible on the page
• No semantic structure around data values

In short, it was Schema… without a trust layer. Google’s response was to call bullshit on what I was publishing.

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How Google Responded

At first, it accepted the markup — 131 valid items were indexed. But over time, as the algorithm deeply evaluated the content and schema, it found a misalignment and started pulling back.

The result: 131 invalid items, and a stall in dataset visibility.

Google didn’t just stop crawling. It re-evaluated the trust signal — and demoted it.

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The Lesson: Trust Must Be Visible and Verifiable

You can’t publish Dataset Schema in a vacuum. If Google can’t see the proof — it stops believing the markup.

That’s why TrustStacker now enforces:

• Inline TrustTags™ for every key fact
• Matching Dataset Schema with identical human-readable citations
• Semantic layout (TrustBlocks) to deliver content Google and AI systems can understand

This Proof Drop isn’t a loss. It’s a warning — and a reminder that trust must be engineered across every layer.

— David W. Bynon