YMYL content and AI search - are the standards higher and how do we meet them?

Yes, AI applies higher standards for YMYL content. Here’s what we’ve documented:

YMYL-specific AI evaluation criteria:

What AI systems specifically check for YMYL:

1. Credentials verification - Can the author’s claims be verified externally?
2. Source authority - Are citations to primary, peer-reviewed sources?
3. Consensus alignment - Does content match medical/financial consensus?
4. Organizational trust - Is the publishing organization credible?
5. Currency - How recent is the information?

Why competitors might be winning:

Your E-E-A-T might be strong but not visible to AI:

• Schema markup missing
• External validation insufficient
• Cross-reference signals weak
• Content format not extractable

Real experience from medical publishing:

What moved our AI citations:

The surprising gaps we found:

1. Authors not verifiable - MDs on staff but no external profiles
2. Review process invisible - Medical board reviewed but not stated on page
3. Citations to secondary sources - Linking to WebMD instead of original studies
4. Stale content - Medically accurate but 3 years old

Fix priorities:

1. Every author needs verifiable external presence
2. Review process must be visible on page
3. Link to primary sources (PubMed, journals)
4. Update dates must be prominent and accurate

Medical content formula:

Author credentials + verifiable profiles + primary sources + recent review = AI trust

Finance YMYL perspective:

Financial content specific requirements:

1. Regulatory compliance disclosure

   • “Not financial advice” disclaimers
   • FINRA/SEC disclosures if applicable
   • Conflict of interest statements

2. Author credential specificity

   • CFP, CFA, CPA designations
   • Verifiable licenses
   • Firm affiliations

3. Source requirements

   • Federal Reserve, SEC, IRS for regulations
   • Academic research for strategies
   • Official company filings for company info

AI sensitivity in finance:

AI is extremely cautious with:

• Investment recommendations
• Tax advice
• Retirement planning
• Insurance guidance

What works for finance AI visibility:

• Educational framing (“How X works” vs. “Do X”)
• Clear disclaimers
• Multiple expert perspectives
• Links to official sources
• Updated for current year tax/regulatory info

The audit question:

Would this pass compliance review at a major financial institution? If not, AI will be cautious citing it.

Legal YMYL observations:

What AI evaluates for legal content:

The jurisdiction challenge:

Legal AI content must be clear about:

• Which jurisdiction (state/federal/country)
• When law was current
• “Consult an attorney” guidance

Successful legal content formula:

“[General principle]. Under [specific jurisdiction] law as of [date], [specific rule]. This may vary by state/situation. Consult a licensed attorney for your circumstances.”

Credential signaling for lawyers:

• Bar number (searchable)
• Firm website (verifiable)
• Martindale-Hubbell rating (external validation)
• Published cases/articles (expertise proof)

AI is conservative with legal:

AI often declines to give legal advice, preferring to cite informational content. Position yourself as educational, not advisory.

Technical trust signals for YMYL:

Page-level signals:

1. Prominent bylines

   • Author name visible
   • Credentials below name
   • Link to full bio

2. Review attribution

   • “Medically reviewed by…”
   • “Fact-checked by…”
   • Review date

3. Source transparency

   • Inline citations
   • Reference section
   • Link to primary sources

4. Update signals

   • Publication date
   • Last updated date
   • “Reviewed for accuracy [date]”

Schema for YMYL:

{
  "@type": "MedicalWebPage",
  "specialty": "Cardiology",
  "author": {...},
  "reviewedBy": {
    "@type": "Person",
    "name": "Dr. Review Person",
    "hasCredential": [...]
  },
  "lastReviewed": "2025-12-01",
  "mainContentOfPage": {...}
}

Trust architecture:

Homepage → About/Team → Author Pages → Content

Each page should link to the next level of credential verification.

Testing YMYL AI trust:

How to audit your YMYL AI visibility:

1. Query AI about your topics “What are the symptoms of [condition]?” Note: Are you cited? How characterized?

2. Query AI about your authors “Who is Dr. [Name]?” Note: Does AI know them? Accurate info?

3. Query AI about your organization “Is [Organization] a reliable source for health information?” Note: What does AI say about you?

Red flags in AI responses:

• Your content not cited for relevant queries
• Competitors cited instead
• AI adds “consult a professional” disclaimers to your cited content (suggests lower trust)
• Your authors unknown to AI

Green flags:

• Cited without disclaimers
• Author named and credentials mentioned
• Organization described as authoritative
• Multiple pieces of your content cited

Monthly audit template:

Track over time to measure improvements.

This identified our gaps. Here’s what we’re fixing:

Gap 1: Author verification

• MDs on staff but limited external profiles
• Fix: Doximity profiles, LinkedIn optimization, hospital directory listings

Gap 2: Review process invisible

• Medical board reviews but not shown on page
• Fix: Add “Medically reviewed by [Name], [Credentials] on [Date]” to all content

Gap 3: Secondary source citations

• Linking to health news sites instead of journals
• Fix: Replace with PubMed, journal direct links

Gap 4: Schema incomplete

• Person schema but missing hasCredential
• Fix: Full MedicalWebPage schema with reviewer attribution

Gap 5: Update signals weak

• Content accurate but no visible review dates
• Fix: Last reviewed dates on all clinical content

Implementation plan:

Week 1-2:

• Author profile audit
• Create missing external profiles
• Update Person schema with credentials

Week 3-4:

• Review attribution visible on all content
• Source citation audit and replacement
• Update date audit

Week 5-6:

• Full schema implementation
• Content freshness updates
• AI visibility testing

Success metrics:

• Monthly AI citation tracking
• Author recognition testing
• Organization trust queries

Key insight:

Our E-E-A-T was real but not visible to AI. Strong credentials mean nothing if AI can’t verify them.

Thanks everyone for the specific guidance!