Generative Engine Optimization (GEO) 2025: The Topical Map Playbook for Earning AI Citations

TL;DR: GEO packages verifiable claims with schema and provenance inside a coherent topical map so AI systems can select, cite and summarize your pages, which compounds topical authority at the cluster level.

You want AI systems to cite your pages, not your competitors. This guide gives you a clear definition, a map-first checklist and reproducible assets you can ship today. It serves B2B SaaS, e-commerce and multi-location teams. You manage clusters, internal links and schema.

If you are new to clusters, start with our topical map and read the topical authority explainer.

Use this page to:

Learn what GEO is and why topical maps raise selection and AI-citation
Ship a hub-and-leaf GEO package with schema and provenance
Measure AI-citation lift at the cluster level with open telemetry

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) packages content, schema and provenance so generative engines select and cite your page as the trusted answer.

GEO works best on a site with strong topical authority and a coherent topical map. Put the canonical claim on the hub, reinforce it with well-linked leaves and expose human-visible anchors plus machine-readable schema. See the topical map methodology for how to define hubs, leaves and depth.

When GEO matters: when ChatGPT, Perplexity, Gemini, and Google AI Overviews assemble concise answers and select a small set of sources for AI-citation
How maps help GEO: a hub claim plus supportive leaves increases trust, improves extraction and makes your cluster easier to cite
One action to start today: publish a provenance-backed Claim JSON-LD on your hub, add FAQPage or HowTo on 3–5 leaves, and link hub ↔ leaves with descriptive anchors

Why do topical maps and topical authority matter for GEO?

GEO selection improves when your hub claim sits inside a coherent topical map and supporting leaves build topical authority across the cluster.

What do engines prefer in a cluster?

A clear hub with a short, verifiable claim; well-linked leaves that cover adjacent subtopics; consistent anchors and schema across the cluster.

Hub with a 40–80 word lead claim and a visible provenance line
Leaves that cover adjacent subtopics and cite the hub
Consistent anchors, schema and terminology across the cluster

How does a topical map raise selection?

Coverage, structure and signals make extraction and citation easier.

Coverage: publish planned leaves so answers have multiple corroborating sources
Structure: link hub ↔ leaves and leaf ↔ leaf with descriptive anchors that match your topical map
Signals: align each page to one query intent, keep IDs stable, and expose provenance on hub and leaves to compound topical authority

Which terms should you align on?

Hub: the canonical page for a topic
Leaf: a subtopic page that supports the hub
Cluster: the hub plus its leaves
Depth: the number of clicks from the hub to a leaf

What failure modes should you avoid?

Strong hub with missing leaves
Leaves that do not cite or link back to the hub
Inconsistent anchors or schema that break claim-to-evidence mapping

What should you implement this week for GEO on a map?

Ship the GEO package on one cluster: one hub and 3–5 leaves.

How do you select the cluster?

Pick a hub with demand in your topical map
Choose 3–5 leaves that answer adjacent intents
Confirm each page has one primary query and a clear role in the cluster

How do you write hub and leaf leads with provenance?

Hub: add a 40–80 word lead claim near the top, then a one-line provenance anchor
Leaves: add 25–60 word supporting leads that reference the hub
Visible anchor pattern: Source: [Team], [YYYY-MM-DD]. Evidence: [#section-id]

How do you add map-aware schema?

Hub: Claim JSON-LD with citation to #evidence, about terms that match your cluster and isPartOf a cluster container
Leaves: FAQPage or HowTo aligned to the supporting lead
Hierarchy: BreadcrumbList that reflects hub → leaf
Stability: keep @id, claim_id and anchors stable

How do you wire internal links?

Hub → every leaf with descriptive anchors that match query intent
Every leaf → hub once near the lead and once in the footer or sidebar
Selective leaf ↔ leaf links where users compare or continue tasks

How do you publish and request indexing?

Update the XML sitemap, lastmod and canonical
Keep hub and leaves in the same cluster path
Request indexing after visible leads and schema are live

How do you start the baseline?

Log hub and leaf URLs, intents and roles
Record week 0 AI-citation presence for hub queries in ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews
Capture internal link counts hub ↔ leaves to track improvements

How do you run a reproducible GEO experiment at the cluster level?

Run a matched test on two comparable clusters. Apply the GEO package to the Treatment cluster and keep the Control cluster unchanged. Measure AI-citation and cluster authority signals for 12 weeks.

How should you design the study?

Use a simple, controlled setup that isolates the GEO package.

Unit: cluster
Arms: one Treatment cluster, one Control cluster
Window: 12 weeks with frozen non-GEO edits
Scope: one language and country

Which clusters qualify?

Pick clusters that are comparable in demand and structure.

Similar demand and seasonality in your topical map
One clear hub plus 3–5 leaves per cluster
Comparable crawl depth and internal link exposure

What goes into the Treatment cluster?

Ship the same package on hub and leaves.

Hub: 40–80 word lead claim with a visible provenance line
Leaves: 25–60 word supporting leads that reference the hub
Schema: Claim on hub, FAQPage or HowTo on leaves, BreadcrumbList on all pages
Links: hub ↔ every leaf, selected leaf ↔ leaf

How do you measure outcomes?

Track AI-citation plus cluster authority KPIs.

Metric	Definition	Target
AI-citation share	Share of answers that cite your hub for tracked queries in ChatGPT, Perplexity, Gemini, and Google AI Overviews	Treatment ≥ Control + 10% for 3 weeks
Answer presence	Weeks with hub cited in answers	≥50% of weeks
Share of voice	Hub citations ÷ all cited domains for the same answers	+5 percentage points by week 8
Topic coverage	Published leaves ÷ planned leaves in the cluster	≥80% by week 8
Hub link ratio	Leaf→hub links ÷ total internal links on leaves	≥0.30
Leaf cohesion	Average leaf↔leaf links per leaf	≥1.0

What files should you publish?

Make the dataset reproducible and map-aware.

telemetry.csv week_start,engine,query,cluster_id,page_url,node_type,variant,answer_present,our_domain_cited,cited_domains,ai_citation_share,sov,notes
pages.csv page_url,cluster_id,node_type(hub|leaf),depth,status(published|planned),inlinks_to_hub,outlinks_to_siblings
queries.csv query,cluster_id,intent(hub|leaf),priority

How should you analyze results?

Compute weekly means by variant. Use difference-in-differences on AI-citation share across weeks 1–12. Confirm movement in share of voice, then validate CTR and assisted conversions.

Which developer artifacts should you copy for map-aware GEO?

Use hub and leaf schema that reflects the cluster. Expose hierarchy, anchors and provenance in human and machine-readable forms.

Below are templates – replace bracketed fields and example.com with your values.

Hub Claim template

{

  "@context": "https://schema.org",

  "@type": "Claim",

  "@id": "https://www.example.com/geo#claim-definition",

  "claimText": "[Your one-sentence canonical claim]",

  "author": { "@type": "Organization", "name": "[Your Brand]", "url": "https://www.example.com" },

  "datePublished": "[YYYY-MM-DD]",

  "citation": { "@type": "CreativeWork", "url": "https://www.example.com/geo#evidence" },

  "about": [

    { "@type": "DefinedTerm", "name": "[Cluster Term 1]", "inDefinedTermSet": "https://www.example.com/topical-map#[cluster-id]" },

    { "@type": "DefinedTerm", "name": "[Cluster Term 2]", "inDefinedTermSet": "https://www.example.com/topical-map#[cluster-id]" }

  ],

  "isPartOf": { "@type": "CreativeWorkSeries", "name": "[Cluster Name]", "url": "https://www.example.com/topical-map#[cluster-id]" },

  "mainEntityOfPage": { "@id": "https://www.example.com/geo" }

}

FAQPage leaf template

{

  "@context": "https://schema.org",

  "@type": "FAQPage",

  "@id": "https://www.example.com/geo/faq#page",

  "mainEntity": [{

    "@type": "Question",

    "@id": "https://www.example.com/geo/faq#q1",

    "name": "[Question users ask about the hub topic]",

    "acceptedAnswer": {

      "@type": "Answer",

      "text": "[25–60 word supporting answer that references the hub claim]",

      "citation": { "@type": "CreativeWork", "url": "https://www.example.com/geo#evidence" },

      "author": { "@type": "Organization", "name": "[Your Brand]" },

      "datePublished": "[YYYY-MM-DD]"

    }

  }],

  "about": [{ "@type": "DefinedTerm", "name": "[Leaf Term]" }],

  "isPartOf": { "@type": "CreativeWorkSeries", "name": "[Cluster Name]", "url": "https://www.example.com/topical-map#[cluster-id]" },

  "mainEntityOfPage": { "@id": "https://www.example.com/geo/faq" }

}

HowTo leaf template

{

  "@context": "https://schema.org",

  "@type": "HowTo",

  "@id": "https://www.example.com/geo/how-to#page",

  "name": "[Task users perform in this cluster]",

  "description": "[Short description aligned to the supporting lead]",

  "totalTime": "PT[minutes]M",

  "step": [

    { "@type": "HowToStep", "name": "Select cluster", "text": "Pick a hub and 3–5 leaves.", "url": "https://www.example.com/geo#select-cluster" },

    { "@type": "HowToStep", "name": "Write leads", "text": "Add a 40–80 word hub lead and shorter leaf leads.", "url": "https://www.example.com/geo#leads" },

    { "@type": "HowToStep", "name": "Add schema", "text": "Use Claim, FAQPage or HowTo and BreadcrumbList.", "url": "https://www.example.com/geo#schema" }

  ],

  "about": [{ "@type": "DefinedTerm", "name": "[Cluster Term]" }],

  "isPartOf": { "@type": "CreativeWorkSeries", "name": "[Cluster Name]", "url": "https://www.example.com/topical-map#[cluster-id]" },

  "author": { "@type": "Organization", "name": "[Your Brand]" },

  "mainEntityOfPage": { "@id": "https://www.example.com/geo/how-to" },

  "citation": { "@type": "CreativeWork", "url": "https://www.example.com/geo#evidence" },

  "datePublished": "[YYYY-MM-DD]"

}

BreadcrumbList template

{

  "@context": "https://schema.org",

  "@type": "BreadcrumbList",

  "itemListElement": [

    { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://www.example.com" },

    { "@type": "ListItem", "position": 2, "name": "[Hub Topic]", "item": "https://www.example.com/geo" },

    { "@type": "ListItem", "position": 3, "name": "[Leaf Name]", "item": "https://www.example.com/geo/[leaf-slug]" }

  ]

}

ItemList on hub template

{

  "@context": "https://schema.org",

  "@type": "ItemList",

  "@id": "https://www.example.com/geo#leaves",

  "itemListElement": [

    { "@type": "ListItem", "position": 1, "url": "https://www.example.com/geo/faq" },

    { "@type": "ListItem", "position": 2, "url": "https://www.example.com/geo/how-to" }

  ]

}

How should you measure GEO and topical authority on a cluster?

Track AI-citation lift on the hub and authority signals across the cluster with a small, map-aware metric set.

What metrics should you track?

Measure hub selection and cluster structure with consistent field names.

Metric	Definition	Calculation	Source	Threshold	Cadence
AI-citation share	Share of answers that cite your hub for tracked queries	Mean of `ai_citation_share` for hub rows	Weekly engine checks	Treatment ≥ Control + 10% for 3 weeks	Weekly
Answer presence	Weeks the hub appears in answer citations	Mean of `answer_present` (0 or 1) for hub rows	Weekly engine checks	≥50% of weeks	Weekly
Share of voice	Hub citations ÷ all cited domains	Mean of `sov` for hub rows	Weekly engine checks	+5 percentage points by week 8	Weekly
Topic coverage	Published leaves ÷ planned leaves in cluster	`published_leaves ÷ planned_leaves`	`pages.csv`	≥80% by week 8	Monthly
Hub link ratio	Leaf→hub links ÷ all internal leaf links	`Σ(inlinks_to_hub) ÷ [Σ(inlinks_to_hub)+Σ(outlinks_to_siblings)]`	`pages.csv`	≥0.30	Monthly
Leaf cohesion	Avg leaf↔leaf links per leaf	`AVERAGE(outlinks_to_siblings)`	`pages.csv`	≥1.0	Monthly
Time to first AI-citation	Weeks until the hub first appears as a cited source	Min week where `answer_present=1`	Telemetry	≤6 weeks	Weekly

How do you structure the datasets?

Keep tidy, joinable files with a shared cluster_id.

telemetry.csv: week_start,engine,query,cluster_id,page_url,node_type,variant,answer_present,our_domain_cited,cited_domains,ai_citation_share,sov,notes
pages.csv: page_url,cluster_id,node_type(hub|leaf),depth,status(published|planned),inlinks_to_hub,outlinks_to_siblings
queries.csv: query,cluster_id,intent(hub|leaf),priority

How do you compute the weekly rollups?

Filter to the hub, then group by week_start and variant.

AI-citation share (hub only)

=AVERAGEIFS(telemetry!K:K, telemetry!A:A, $A2, telemetry!G:G, $B2, telemetry!F:F, "hub")

Answer presence (hub only)

=AVERAGEIFS(telemetry!H:H, telemetry!A:A, $A2, telemetry!G:G, $B2, telemetry!F:F, "hub")

Share of voice (hub only)

=AVERAGEIFS(telemetry!L:L, telemetry!A:A, $A2, telemetry!G:G, $B2, telemetry!F:F, "hub")

How do you track cluster authority KPIs?

Compute coverage and link structure from pages.csv.

Topic coverage

=COUNTIFS(pages!C:C,"leaf", pages!E:E,"published") / COUNTIFS(pages!C:C,"leaf")

Hub link ratio

=SUMIFS(pages!F:F, pages!C:C,"leaf") / (SUMIFS(pages!F:F, pages!C:C,"leaf") + SUMIFS(pages!G:G, pages!C:C,"leaf"))

Leaf cohesion

=AVERAGEIFS(pages!G:G, pages!C:C,"leaf")

What alert rules should you set?

Use simple, testable triggers tied to actions.

Success: hub AI-citation share ≥ Control + 10% for 3 consecutive weeks
Coverage gap: topic coverage < 70% by week 6
Structure gap: hub link ratio < 0.20 or leaf cohesion < 0.5
Regression: week-over-week drop ≥20% in hub AI-citation share
Outcome check: CTR or assisted conversions down ≥10% vs baseline after 4 weeks

What cadence should you follow?

Run a steady loop that favors small, fast iterations.

Weekly: scrape answers, update telemetry, review hub metrics, log notes
Biweekly: harden anchors on weak leaves, add missing leaf links, adjust leads
Monthly: update coverage, link ratios and cohesion; decide to extend or end the test

What should you do next?

Publish one canonical hub, fill 3–5 leaves, ship schema and provenance, then run the 12-week cluster test and monitor AI-citation and authority signals.

What are the immediate actions?

Commit to a short list that moves the cluster.

Finalize the hub’s 40–80 word claim with a visible provenance line
Add Claim JSON-LD on the hub, FAQPage or HowTo on leaves, plus BreadcrumbList
Wire hub ↔ leaves and selective leaf ↔ leaf links with descriptive anchors
Publish datasets and start weekly telemetry

How do you move from pilot to scale?

Replicate the package to similar clusters while keeping IDs stable.

Roll out to two more clusters with comparable demand
Keep IDs, anchors and cluster slugs stable across deploys
Promote the open dataset and methods to invite third-party replication

What does success look like?

Define success so teams can declare an outcome.

The hub earns consistent AI-citation presence
Topic coverage reaches or exceeds 80 percent
Hub link ratio and leaf cohesion meet targets
CTR and assisted conversions trend up as the cluster matures

What CSV starters should you use for telemetry and maps?

Use tidy, joinable CSVs for telemetry, pages and queries so your team can compute AI-citation and cluster KPIs without friction.

Headers to copy

telemetry.csv

week_start,engine,query,cluster_id,page_url,node_type,variant,answer_present,our_domain_cited,cited_domains,ai_citation_share,sov,notes

pages.csv

page_url,cluster_id,node_type,depth,status,inlinks_to_hub,outlinks_to_siblings

queries.csv

query,cluster_id,intent,priority

Data dictionary (concise)

week_start ISO date
engine ChatGPT, Perplexity, Gemini, Google AI Mode, Google AI Overviews
query exact query text
cluster_id stable cluster key
page_url canonical URL
node_type hub or leaf
variant Treatment or Control
answer_present 0 or 1
our_domain_cited 0 or 1
cited_domains semicolon separated
ai_citation_share 0–1
sov share of voice 0–1
notes free text
depth clicks from hub
status published or planned
inlinks_to_hub count from leaf to hub
outlinks_to_siblings count of leaf↔leaf links
intent hub or leaf
priority 1 is highest

Download the starter files

telemetry_template.csv
pages_template.csv
queries_template.csv

We’re using example.com in these templates. Replace with your domain when you roll out.

What is the cluster QA checklist before you publish?

Run this quick, map-first QA so engines can extract, cite and verify your cluster.

Hub checks

Lead claim is 40–80 words near the top
Visible provenance line with author, date and #evidence anchor
Claim JSON-LD present with stable @id and citation to #evidence
Breadcrumb shows hub position in the cluster
ItemList of leaves exists or is planned

Leaf checks

Supporting lead is 25–60 words and references the hub
Leaf links to hub near the lead and once more on the page
Leaf links to at least one sibling where relevant
FAQPage or HowTo JSON-LD matches visible copy
about terms align to cluster vocabulary

Anchor and ID checks

#evidence resolves to the exact claim text
@id, claim_id, cluster_id are stable across deploys
No duplicate anchors in hub or leaves

Crawl and index checks

Canonicals and sitemaps updated with lastmod
Robots allow hub and leaves
Internal links use descriptive anchors that match intent

Telemetry readiness

telemetry.csv seeded with week 0 rows
pages.csv lists hub and leaves with correct node types
queries.csv lists hub and leaf intents with priorities

Provenance and dataset

/provenance.json lists the hub claim with checksum_sha256 of telemetry
Dataset location is stable and publicly accessible
Dates in visible copy match JSON-LD and provenance

When this QA passes, publish the cluster, request indexing and start the 12-week measurement loop.

How do you set up RAG so it cites your hub and leaves?

Use narrow retrieval, boost cluster terms and anchors, and enforce short answers with citations.

What retrieval steps should you use?

Normalize intent, restrict scope to the cluster, and boost anchors.

Normalize the query to intent
Filter search to your domain and the cluster path
Boost passages that contain claim_id, #evidence, or cluster DefinedTerm names
Return top passages with their URLs and fragment IDs

What system and user prompts work?

Constrain answers to provided context and require anchor citations.

System prompt

Answer from the provided context only. Cite sources as [url#anchor]. Keep answers to 80 words unless asked for more. If context is insufficient, say what is missing.

User prompt template

Question: {{user_query}}

Context:

{{top_k_passages_with_urls_and_anchors}}

Task: Give a short, direct answer with 1–3 citations to the most relevant anchors.

How do you check output quality?

Validate answer shape and citations before you show results.

Expected answer shape

{

  "answer_text": "[<= 80 words answering the question]",

  "citations": [

    { "url": "https://www.example.com/geo#evidence", "claim_id": "claim-definition" }

  ]

}

Acceptance checks

Answer length ≤ 80 words
At least one citation to the hub #evidence anchor
Optional second citation to a leaf that supports the hub
No citations to off-domain pages unless the task requires it

Where should you place schema and provenance in common CMSs?

Inject one JSON-LD block per type, expose a stable #evidence anchor, and serve a small provenance index at the site root.

How do you add this in WordPress?

Use theme head or a code-injection plugin to add JSON-LD and anchors.

Insert <script type="application/ld+json">…</script> in the theme head
Place the visible claim and #evidence near the top of the hub
Keep one JSON-LD block per type on each page

Claim snippet (template)

<script type="application/ld+json">

{

  "@context":"https://schema.org",

  "@type":"Claim",

  "@id":"https://www.example.com/geo#claim-definition",

  "claimText":"[Your one-sentence canonical claim]",

  "author":{"@type":"Organization","name":"[Your Brand]"},

  "datePublished":"[YYYY-MM-DD]",

  "citation":{"@type":"CreativeWork","url":"https://www.example.com/geo#evidence"},

  "mainEntityOfPage":{"@id":"https://www.example.com/geo"}

}

</script>

<p id="evidence">[Visible claim text that matches the JSON-LD]</p>

How do you add this in Shopify?

Edit theme.liquid or section templates for products and articles.

Paste JSON-LD in <head> of theme.liquid or in a section for that template
Add the #evidence anchor inside the content block
Avoid duplicating the same JSON-LD type on the same URL

How do you add this in Webflow or Wix?

Use the page-level custom code area.

Paste JSON-LD before </body> on the hub and each leaf
Add #evidence to the visible claim paragraph
Validate with a structured data tester

How do you add this in headless setups?

Render JSON-LD at build time and serve a provenance file.

Next.js or Gatsby: render <script type="application/ld+json"> in <Head> per route
Serve /provenance.json at the site root with stable claim records
Keep @id, claim_id, and anchors stable across deploys

Provenance index (template)

{

  "publisher": "[Your Brand]",

  "updated": "[YYYY-MM-DD]",

  "claims": [

    {

      "claim_id": "claim-definition",

      "page_url": "https://www.example.com/geo",

      "anchor": "#evidence",

      "claim": "[Your one-sentence canonical claim]",

      "author": "[Your Team]",

      "date": "[YYYY-MM-DD]",

      "dataset_url": "https://www.example.com/dataset/telemetry.csv",

      "checksum_sha256": "[sha256-of-telemetry.csv]"

    }

  ]

}

What validation steps should you run?

Check anchors, IDs and crawl signals before publishing.

Click the #evidence anchor and confirm it resolves to the claim
Validate JSON-LD and ensure no duplicate types per page
Update sitemap and lastmod, then request indexing

How do you troubleshoot GEO when a cluster fails to earn citations?

Work from evidence to packaging to structure. Fix the smallest thing that blocks selection.

What symptoms signal selection problems?

A missing hub presence, unstable anchors or weak structure.

Hub absent from answer citations for tracked queries
#evidence anchor not resolving to the visible claim
Conflicting canonicals or blocked pages in robots
Duplicate JSON-LD types on the same URL
Leaves published without links back to the hub

What fixes usually work first?

Tighten the claim, anchors and schema before bigger changes.

Place a 40–80 word claim at the top of the hub
Add a visible provenance line with author, date and #evidence
Map Claim.citation.url to the exact #evidence anchor
Keep one JSON-LD block per type per page
Add descriptive links hub ↔ leaves and selective leaf ↔ leaf
Update sitemap and lastmod, then request indexing

What diagnostics should you run?

Confirm crawl, render and extraction paths.

Fetch as mobile and confirm final HTML contains JSON-LD and #evidence
Validate structured data and remove duplicates
Verify canonical, hreflang and robots rules
Compare clusters for crawl depth and internal link exposure
Record one week of baseline telemetry before copy changes

What should you avoid?

Large rewrites or stacked interventions during the test.

Moving URLs or anchors during the 12-week window
Adding or removing multiple JSON-LD types at once
Running overlapping experiments on the same cluster

How do you govern IDs, versioning and reproducibility?

Use stable IDs, explicit versions and a public change log so others can audit and replicate results.

What needs stable IDs?

Claims, pages and clusters need stable identifiers.

claim_id for each visible claim
@id in JSON-LD for hub and leaves
cluster_id shared across telemetry and pages

How do you version datasets and claims?

Create immutable snapshots and update checksums.

Name datasets with dates, for example telemetry_2025-09-01.csv
Maintain a moving telemetry.csv that points to the latest snapshot
Recompute checksum_sha256 in /provenance.json when data changes
Add a version field to claims when the wording changes

Provenance entry with version (template)

{

  "claim_id": "claim-definition",

  "version": "v1.1",

  "page_url": "https://www.example.com/geo",

  "anchor": "#evidence",

  "author": "[Your Team]",

  "date": "2025-09-01",

  "dataset_url": "https://www.example.com/dataset/telemetry_2025-09-01.csv",

  "checksum_sha256": "[sha256-of-telemetry_2025-09-01.csv]"

}

What change log should you keep?

Log every change that can affect selection or measurement.

Date, field, page, reason, expected effect
Lead claim edits and schema updates
New leaves published or links added
Experiment pauses or protocol deviations

methods.md snippet

## Change log

2025-09-01: Hub lead claim tightened from 92 to 68 words. Updated Claim.citation to #evidence. Expect improved extraction.

2025-09-08: Added leaf-to-leaf links between FAQ and HowTo. Expect higher cohesion.

2025-09-15: Published leaf “geo vs seo”. No other changes.

How do you retire or merge clusters?

Minimize disruption and keep provenance intact.

Freeze edits, export final telemetry and publish a closing note
301 redirect deprecated leaves to the hub or the new cluster hub
Keep claim_id stable if the claim persists; bump version if wording changes
Retain old datasets and checksums for auditability

Which FAQs do teams ask about GEO and topical maps?

Is GEO the same as AEO?

No. They overlap but differ in focus.

Scope: AEO covers snippets, voice and AI chat. GEO targets LLM-based generative engines.
Methods: AEO favors concise Q&A and structured data. GEO adds provenance-backed claims, stable IDs and copyable schema.
Measurement: AEO tracks snippet presence and answer selection. GEO tracks AI-citation share, share of voice and time to first citation.

Use AEO if your program includes snippets, voice and chat.

Use GEO if your priority is selection and citation inside generative engines.

Does GEO replace SEO?

No. SEO earns rankings and clicks. GEO packages claims, schema and provenance so engines cite you.

How many leaves should a cluster have?

Start with 3–5 leaves that cover adjacent intents to the hub.

How fast can you see AI-citation lift?

Many teams see movement in 3–6 weeks. Use the 12-week protocol to confirm.

Do all pages need schema?

Put a Claim on the hub. Use FAQPage or HowTo on leaves. Keep IDs and anchors stable.

Can GEO work without a topical map?

Yes, but clusters improve selection. Hubs and leaves create corroboration and cleaner extraction.

What glossary should teams share for GEO and topical maps?

Align on these short definitions to reduce implementation drift.

Generative Engine Optimization (GEO): Packaging content, schema and provenance so AI systems select and cite your page.
AI-citation: A generative answer credits your domain as a source.
Hub: The canonical page that carries the cluster’s lead claim.
Leaf: A subtopic page that supports the hub.
Cluster: The hub plus its leaves that cover one topic.
Topic coverage: Published leaves ÷ planned leaves in the cluster.
Hub link ratio: Leaf→hub links ÷ all internal links on leaves.
Leaf cohesion: Average leaf↔leaf links per leaf.
Claim: The one-sentence, canonical statement you want engines to select.
Evidence anchor: The on-page fragment ID that resolves to the claim text.
Provenance: Verifiable author, date and evidence that back a claim.
claim_id: Stable identifier for a claim used in schema and telemetry.
cluster_id: Stable key that joins telemetry, pages and queries.
DefinedTerm: Schema term that tags pages with cluster vocabulary.
BreadcrumbList: Schema that exposes hub → leaf hierarchy.
Telemetry: Weekly rows logging engine, query, page, citations and notes.
Share of voice: Your citations ÷ all cited domains for the same answers.
Treatment cluster: The cluster that receives the GEO package.
Control cluster: The comparable cluster left unchanged during the test.