SEO & AEO Best Practices

Principles for optimizing content for both traditional search engines (SEO) and AI-powered answer engines (AEO). Includes Google's EEAT guidelines and structured data implementation.

When to Apply

Reference these guidelines when:

• Implementing metadata and Open Graph tags
• Creating sitemaps and robots.txt
• Adding JSON-LD structured data
• Optimizing content for featured snippets
• Preparing content for AI assistants (ChatGPT, Perplexity, etc.)
• Evaluating content quality using EEAT principles

Core Concepts

SEO (Search Engine Optimization)

Optimizing content to rank well in traditional search results (Google, Bing).

AEO (Answer Engine Optimization)

Optimizing content to be selected as authoritative answers by AI systems.

EEAT (Experience, Expertise, Authoritativeness, Trustworthiness)

Google's framework for evaluating content quality.

References

Start with the one reference that matches the task, such as technical SEO, structured data, EEAT, or AI-answer readiness. See references/ for detailed guidance:

• references/eeat-principles.md — EEAT implementation and author schema
• references/structured-data.md — JSON-LD patterns (Article, FAQ, Breadcrumb, Product)
• references/technical-seo.md — Technical SEO checklist (metadata, sitemaps, hreflang, robots.txt)
• references/aeo-considerations.md — AI/AEO considerations (AI Overviews, crawler management)

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Supporting file: references/aeo-considerations.md

AI/AEO Considerations

Answer Engine Optimization (AEO) prepares content to be selected as authoritative answers by AI systems like ChatGPT, Perplexity, Google AI Overviews, and Bing Copilot.

How AI Selects Answers

AI systems evaluate content based on:

1. Clarity: Is the answer direct and easy to extract?
2. Authority: Is the source trustworthy?
3. Comprehensiveness: Does it fully address the question?
4. Recency: Is the information up to date?
5. Structure: Can the AI parse and understand it?

Content Structure for AI

Direct Answers First

Lead with the answer, then explain.

Bad:

The history of JavaScript dates back to 1995 when Brendan Eich... [500 words later] ...JavaScript runs in the browser.

Good:

JavaScript is a programming language that runs in web browsers. It was created in 1995 by Brendan Eich...

Clear Headings

Use descriptive H2/H3 headings that match user questions.

Bad: "Overview" → "Details" → "More Information" Good: "What is X?" → "How does X work?" → "When should you use X?"

Lists and Tables

AI extracts structured information more easily than prose.

## Benefits of Structured Content

- **Reusability:** Use content across channels
- **Flexibility:** Change presentation without changing content
- **Scalability:** Manage large content volumes

FAQ Format

Question-answer pairs are ideal for AI extraction.

// Schema for AI-friendly FAQs
defineType({
  name: 'faq',
  type: 'document',
  fields: [
    defineField({ name: 'question', type: 'string' }),
    defineField({ name: 'answer', type: 'text' }),
    defineField({ name: 'category', type: 'reference', to: [{ type: 'faqCategory' }] }),
  ]
})

Technical Implementation

Structured Data (Critical)

JSON-LD helps AI understand content type and relationships.

// FAQ structured data
const faqSchema = {
  "@context": "https://schema.org",
  "@type": "FAQPage",
  mainEntity: faqs.map(faq => ({
    "@type": "Question",
    name: faq.question,
    acceptedAnswer: {
      "@type": "Answer",
      text: faq.answer
    }
  }))
}

Canonical Content

Ensure AI finds your authoritative version, not copies.

• Set canonical URLs
• Avoid duplicate content across pages
• Use rel="canonical" for syndicated content

Freshness Signals

AI systems prefer current information.

• Display publish and update dates prominently
• Update content regularly with substantive changes (superficial updates like changing dates without meaningful edits can be counterproductive)
• Use dateModified in structured data

Content Quality Signals

Author Credentials

AI systems increasingly check author authority.

• Display author name and credentials
• Link to author profiles
• Include author structured data

Citations and Sources

Linking to authoritative sources increases trust.

• Cite primary sources
• Link to studies, documentation, official sources
• Avoid circular citations (sites citing each other)

Comprehensive Coverage

AI prefers content that fully answers questions.

• Cover related questions users might have
• Include definitions for technical terms
• Address common misconceptions

Google AI Overviews

Google's AI Overviews (formerly SGE) now appear in many search results. To optimize:

• Be the cited source: AI Overviews cite specific pages. Concise, authoritative answers increase citation likelihood.
• Structure for extraction: Use clear headings, direct answers, and lists that AI can easily parse.
• Cover follow-up questions: AI Overviews often address related queries. Anticipate and answer them on the same page or link to dedicated pages.
• Monitor in Search Console: Google Search Console provides data on AI Overview impressions and clicks.

AI Crawler Management

Make conscious decisions about which AI systems can crawl your content:

• robots.txt directives: Use User-agent: GPTBot, ClaudeBot, PerplexityBot, Google-Extended to control access.
• Allowing crawlers increases chances of being cited as a source in AI responses.
• Blocking crawlers prevents content from being used in AI training (but may reduce AI citations).
• Review your policy regularly — this is one of the most actively evolving areas of SEO.

Measuring AEO Success

Monitor AI Mentions

Track when AI assistants cite your content:

• Use Google Search Console's AI Overview data for impression and click tracking
• Monitor referral traffic from AI platforms (Perplexity, ChatGPT, Bing Copilot)
• Search for your brand + "according to" in AI assistants
• Consider third-party AEO tracking tools for comprehensive monitoring

Track Zero-Click Queries

If AI answers questions directly, traditional rankings matter less.

Featured Snippet Capture

Featured snippets often become AI answers. Track which you own.

AEO vs SEO Balance

AEO and SEO largely align—quality content serves both. Key differences:

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Supporting file: references/eeat-principles.md

EEAT Principles

Google's EEAT framework (Experience, Expertise, Authoritativeness, Trustworthiness) guides how content quality is evaluated. This applies to both SEO rankings and AI answer selection.

The Four Pillars

Experience

First-hand or life experience with the topic.

Signals:

• Personal anecdotes and case studies
• "I tested this" content
• Real-world results and screenshots
• User-generated reviews

Implementation:

• Include author bios with relevant experience
• Add "About the Author" sections
• Feature customer testimonials
• Show real examples, not just theory

Expertise

Knowledge and skill in the subject area.

Signals:

• Credentials and qualifications
• Depth of content coverage
• Technical accuracy
• Citations to authoritative sources

Implementation:

• Display author credentials
• Link to primary sources
• Cover topics comprehensively
• Keep content technically accurate and updated

Authoritativeness

Recognition as a go-to source in the field.

Signals:

• Backlinks from respected sites
• Mentions in industry publications
• Social proof and follower counts
• Brand recognition

Implementation:

• Build thought leadership content
• Contribute to industry publications
• Maintain consistent publishing
• Develop recognizable brand voice

Trustworthiness

Accuracy, transparency, and legitimacy.

Signals:

• Clear authorship and contact info
• Accurate, fact-checked content
• Secure website (HTTPS)
• Privacy policy and terms

Implementation:

• Display clear author attribution
• Include publication and update dates
• Provide contact information
• Use HTTPS and maintain security

Sanity Implementation

// Author schema with EEAT signals
defineType({
  name: 'author',
  type: 'document',
  fields: [
    defineField({ name: 'name', type: 'string' }),
    defineField({ name: 'role', type: 'string' }),
    defineField({ name: 'bio', type: 'text' }),
    defineField({ name: 'credentials', type: 'array', of: [{ type: 'string' }] }),
    defineField({ name: 'image', type: 'image' }),
    // sameAs: used for schema.org Person structured data output
    defineField({ name: 'sameAs', type: 'array', of: [{ type: 'url' }],
      description: 'Canonical profile URLs (LinkedIn, Twitter, etc.) for schema.org Person'
    }),
    // socialLinks: used for display purposes (platform icons, labels)
    defineField({
      name: 'socialLinks',
      type: 'array',
      of: [{ type: 'object', fields: [
        defineField({ name: 'platform', type: 'string' }),
        defineField({ name: 'url', type: 'url' })
      ]}],
      description: 'Social links for display in the UI. Use sameAs for structured data output.'
    }),
  ]
})

// Content with EEAT metadata
defineType({
  name: 'post',
  fields: [
    defineField({ name: 'author', type: 'reference', to: [{ type: 'author' }] }),
    defineField({ name: 'publishedAt', type: 'datetime' }),
    defineField({ name: 'updatedAt', type: 'datetime' }),
    defineField({ 
      name: 'reviewedBy', 
      type: 'reference', 
      to: [{ type: 'author' }],
      description: 'Expert reviewer for fact-checking'
    }),
    defineField({
      name: 'sources',
      type: 'array',
      of: [{ type: 'url' }],
      description: 'Citations and references'
    }),
  ]
})

YMYL Considerations

"Your Money or Your Life" topics (health, finance, legal, safety) require extra EEAT rigor:

• Medical content reviewed by healthcare professionals
• Financial advice from certified experts
• Legal content reviewed by attorneys
• Clear disclaimers where appropriate

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Supporting file: references/structured-data.md

Structured Data (JSON-LD)

Structured data helps search engines and AI understand your content. JSON-LD is the recommended format.

Why Structured Data Matters

• Rich snippets: Enhanced search result appearance
• Knowledge panels: Featured information boxes
• AI training: Better content understanding
• Voice search: Answer selection for voice queries

Common Schema Types

Article / Blog Post

import { Article, WithContext } from 'schema-dts'

const articleSchema: WithContext<Article> = {
  "@context": "https://schema.org",
  "@type": "Article",
  headline: post.title,
  description: post.excerpt,
  image: post.image?.url,
  datePublished: post.publishedAt,
  dateModified: post.updatedAt,
  author: {
    "@type": "Person",
    name: post.author.name,
    url: post.author.url
  },
  publisher: {
    "@type": "Organization",
    name: "Your Company",
    logo: {
      "@type": "ImageObject",
      url: "https://example.com/logo.png"
    }
  }
}

FAQ Page

import { FAQPage, WithContext } from 'schema-dts'

const faqSchema: WithContext<FAQPage> = {
  "@context": "https://schema.org",
  "@type": "FAQPage",
  mainEntity: faqs.map(faq => ({
    "@type": "Question",
    name: faq.question,
    acceptedAnswer: {
      "@type": "Answer",
      text: faq.answer  // Plain text, use pt::text() in GROQ
    }
  }))
}

Organization

import { Organization, WithContext } from 'schema-dts'

const orgSchema: WithContext<Organization> = {
  "@context": "https://schema.org",
  "@type": "Organization",
  name: "Your Company",
  url: "https://example.com",
  logo: "https://example.com/logo.png",
  sameAs: [
    "https://twitter.com/company",
    "https://linkedin.com/company/company"
  ],
  contactPoint: {
    "@type": "ContactPoint",
    telephone: "+1-555-555-5555",
    contactType: "customer service"
  }
}

Product

import { Product, WithContext } from 'schema-dts'

const productSchema: WithContext<Product> = {
  "@context": "https://schema.org",
  "@type": "Product",
  name: product.name,
  description: product.description,
  image: product.images,
  offers: {
    "@type": "Offer",
    price: product.price,
    priceCurrency: "USD",
    availability: "https://schema.org/InStock"
  },
  aggregateRating: product.rating ? {
    "@type": "AggregateRating",
    ratingValue: product.rating.average,
    reviewCount: product.rating.count
  } : undefined
}

Breadcrumb

import { BreadcrumbList, WithContext } from 'schema-dts'

const breadcrumbSchema: WithContext<BreadcrumbList> = {
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  itemListElement: breadcrumbs.map((crumb, index) => ({
    "@type": "ListItem",
    position: index + 1, // schema.org positions are 1-based
    name: crumb.title,
    item: `https://example.com${crumb.path}`
  }))
}

Combining Multiple Schemas (@graph)

Real-world pages often need multiple schema types. Use @graph to combine them. The @context is defined once at the top level — omit it from individual schema generators when used inside @graph:

const pageSchema = {
  "@context": "https://schema.org",
  "@graph": [
    generateArticleSchema(post),      // No @context needed here
    generateBreadcrumbSchema(breadcrumbs),
    generateOrganizationSchema(),
  ]
}

Implementation in Next.js

// Component to render JSON-LD
// Ensure data comes from trusted sources (your CMS).
// If data could contain user-generated content, strip HTML tags
// and escape special characters before passing to JSON.stringify.
function JsonLd({ data }: { data: WithContext<Thing> }) {
  return (
    <script
      type="application/ld+json"
      dangerouslySetInnerHTML={{ __html: JSON.stringify(data) }}
    />
  )
}

// Usage in page
export default function PostPage({ post }) {
  return (
    <>
      <JsonLd data={generateArticleSchema(post)} />
      <article>...</article>
    </>
  )
}

GROQ for Plain Text

Structured data often needs plain text, not rich text:

*[_type == "faq"]{
  question,
  "answer": pt::text(answerRichText)  // Convert Portable Text to plain string
}

Testing Tools

• Google Rich Results Test (https://search.google.com/test/rich-results)
• Schema.org Validator (https://validator.schema.org/)

---

Supporting file: references/technical-seo.md

Technical SEO Checklist

Essential technical SEO elements for modern web applications.

Table of Contents

• Metadata
• Sitemaps
• Canonical URLs
• Redirects
• Performance
• Robots.txt
• International SEO

Metadata

Title Tags

• Unique per page
• 50-60 characters
• Primary keyword near the beginning
• Brand name at the end (optional)

Meta Descriptions

• Unique per page
• 150-160 characters
• Include call-to-action
• Contain relevant keywords

Open Graph

<meta property="og:title" content="Page Title" />
<meta property="og:description" content="Description" />
<meta property="og:image" content="https://example.com/image.jpg" />
<meta property="og:url" content="https://example.com/page" />
<meta property="og:type" content="article" />

Sanity + Next.js Implementation

export async function generateMetadata({ params }): Promise<Metadata> {
  const { data } = await sanityFetch({
    query: PAGE_QUERY,
    stega: false,  // Critical: no stega in metadata
  })
  
  return {
    title: data.seo?.title || data.title,
    description: data.seo?.description,
    openGraph: {
      images: data.seo?.image ? [{
        url: urlFor(data.seo.image).width(1200).height(630).url(),
        width: 1200,
        height: 630,
      }] : [],
    },
    robots: data.seo?.noIndex ? 'noindex' : undefined,
  }
}

Sitemaps

Dynamic sitemap from CMS content:

// app/sitemap.ts
import { MetadataRoute } from 'next'

export default async function sitemap(): Promise<MetadataRoute.Sitemap> {
  const pages = await client.fetch(`
    *[_type in ["page", "post"] && defined(slug.current) && seo.noIndex != true]{
      "url": select(
        _type == "page" => "/" + slug.current,
        _type == "post" => "/blog/" + slug.current
      ),
      _updatedAt
    }
  `)
  
  return pages.map(page => ({
    url: `https://example.com${page.url}`,
    lastModified: new Date(page._updatedAt),
    // Note: changeFrequency and priority are largely ignored by Google
    // but may be used by other search engines
  }))
}

Canonical URLs

Prevent duplicate content issues:

export async function generateMetadata({ params }): Promise<Metadata> {
  return {
    alternates: {
      canonical: `https://example.com/${params.slug}`,
    },
  }
}

Redirects

CMS-managed redirects:

// next.config.ts
async redirects() {
  const redirects = await client.fetch(`
    *[_type == "redirect" && isEnabled == true]{
      source,
      destination,
      permanent
    }
  `)
  return redirects
}

Performance

Core Web Vitals (https://web.dev/articles/defining-core-web-vitals-thresholds) impact rankings:

• LCP (Largest Contentful Paint): < 2.5s
• INP (Interaction to Next Paint): < 200ms
• CLS (Cumulative Layout Shift): < 0.1

Image Optimization (Next.js example)

• Use next/image with Sanity URL builder
• Serve WebP/AVIF formats
• Implement LQIP blur placeholders
• Set explicit dimensions

Font Loading (Next.js example)

// Prevent layout shift
import { Inter } from 'next/font/google'
const inter = Inter({ subsets: ['latin'], display: 'swap' })

Robots.txt

# public/robots.txt
User-agent: *
Allow: /
Disallow: /api/
Disallow: /studio/

# AI crawlers — allow or block based on your content strategy
# Uncomment to block specific AI crawlers:
# User-agent: GPTBot
# Disallow: /
# User-agent: ClaudeBot
# Disallow: /
# User-agent: PerplexityBot
# Disallow: /
# User-agent: Google-Extended
# Disallow: /

Sitemap: https://example.com/sitemap.xml

AI crawler considerations: Decide whether AI training crawlers should access your content. Blocking Google-Extended prevents AI training use while still allowing Google Search indexing. Review your policy regularly as this landscape evolves.

International SEO (hreflang)

For multi-language sites, implement hreflang tags to indicate language/region variants:

export async function generateMetadata({ params }: { params: Promise<{ lang: string; slug: string }> }): Promise<Metadata> {
  const { lang, slug } = await params
  return {
    alternates: {
      canonical: `https://example.com/${lang}/${slug}`,
      languages: {
        'en': `https://example.com/en/${slug}`,
        'de': `https://example.com/de/${slug}`,
        'x-default': `https://example.com/en/${slug}`,
      },
    },
  }
}

Include all language variants in sitemaps with hreflang annotations for proper indexing.