SEO playbook to get cited by ChatGPT, Perplexity, Claude, Google AI Overviews and Bing

Framework

The AI citation stack

AI visibility is not one trick. It is a stack. If one layer is weak, the answer engine has less reason to choose you.

Layer	What it answers	What to build
Entity	Who are you and what are you known for?	About page, author page, organisation schema, consistent naming.
Answer	Can you answer the exact question clearly?	Direct answer blocks, comparison pages, FAQs, explainers.
Evidence	Why should this be trusted?	Examples, dates, data, case studies, reviews, quotes, screenshots.
Surface	Where does the proof live?	Your site, LinkedIn, trusted publications, directories, docs, GitHub where relevant.
Signals	Is this alive and referenced?	Fresh updates, shares, internal links, backlinks, social discussion.
Monitoring	What do AI engines actually say?	Repeatable prompt tests, citations log, correction backlog.

Step 1

Run AI visibility reconnaissance

Start by seeing the world through the answer engine. Do not ask only for your brand. Ask category questions, comparison questions, location questions, “best for” questions and “who is trusted for” questions.

Goal: capture where you appear, where competitors appear, what sources are cited and which answers look weak or outdated.

Stay in this chat thread. Task: Map current AI visibility for [BRAND] in [INDUSTRY / LOCATION]. Ask and answer these: 1. Who are the top [SERVICE / PRODUCT] providers for [AUDIENCE] in [LOCATION]? 2. Which brands are mentioned repeatedly? 3. Which domains or sources are cited repeatedly? 4. Where does [BRAND] appear, if at all? 5. What looks weak, outdated, thin or unsupported? Output: Question | AI answer summary | Brands named | Sources cited | Gaps | Opportunity score 1-10

Step 2

Build a question map, not a keyword list

Keywords still matter, but AI answers are driven by questions, tasks and comparisons. Your map should include the questions people ask before they know your name, while they compare options and when they need proof.

Category questions: “What is the best way to…”
Comparison questions: “Which is better for…”
Trust questions: “Who is credible for…”
Problem questions: “How do I fix…”
Decision questions: “What should I choose if…”

From the recon data, create a question map for [BRAND]. Group questions by: – Awareness – Comparison – Trust – Buying / decision – Post-purchase / implementation For each question, output: Question | Current AI answer quality | Sources cited | Missing evidence | Best content asset to create | Priority 1-10

Step 3

Create citation-ready answer assets

A citation-ready page is not a generic blog post. It is a clean answer asset that a model can lift from without guessing. Put the answer early, show your criteria, back claims with proof and make the page easy to parse.

Block	Purpose	What to include
Direct answer	Gives the model a clean summary.	One to three sentences at the top.
Criteria	Shows how judgement was made.	What matters, who it is for, limits and assumptions.
Evidence	Makes claims usable.	Dates, examples, results, customer proof, screenshots, case notes.
Entity links	Connects meaning.	Internal links, author page, product page, related explainers.
FAQ	Captures long-tail answer patterns.	Short, direct, specific answers.

Create an AI-citable content brief for this question: [QUESTION] The page must include: – Direct answer in the first 80 words – Who this is for – Decision criteria – Evidence points needed – Comparison angles – Common objections – FAQ section – Internal links to supporting pages – Pull quote candidates – Suggested schema Output: Page title | Meta description | H1 | Opening answer | Section plan | Evidence checklist | FAQ | CTA

Step 4

Add proof and quality gates

Modern AI visibility is a trust problem. If your page says “we are best” but provides no evidence, it is weak. If it gives dated proof, named authorship, clear scope and comparison criteria, it becomes much easier to reuse.

Add an author or organisation identity that matches the site entity.
Use visible “last updated” dates where freshness matters.
Include proof blocks: data, examples, screenshots, case studies, testimonials or process evidence.
Use structured data where appropriate: Article, FAQPage, Organisation, Person, Product or HowTo.
Create a quality gate before publishing: factual check, source check, claim check, link check and clarity check.

Act as an AI visibility quality gate. Review this content for citation readiness: [PASTE CONTENT] Score 1-10 on: 1. Direct answer clarity 2. Entity clarity 3. Evidence strength 4. Freshness 5. Comparison usefulness 6. Source and link quality 7. AI extractability Return: Scorecard | Weak claims | Missing proof | Rewrite suggestions | Publish / revise decision

Step 5

Publish where trust can compound

Do not rely on one page. Build a small web of consistent evidence. Your own site is the source of truth, but answer engines also pick up authority from recognised surfaces, third-party mentions and public discussion.

Surface	Best use	Signal created
Your site	Canonical answer asset and proof hub.	Entity and source of truth.
LinkedIn / founder posts	Point of view, launch commentary, social proof.	Freshness and identity.
Industry sites	Guest explainers, interviews, case studies.	Third-party authority.
Directories / profiles	Consistent brand facts and service/category definitions.	Entity consistency.
Docs / GitHub / changelogs	Technical proof, product updates, transparent build history.	Verification and recency.

Do not fake it: do not manufacture reviews, spam directories or spin low-quality articles. The durable play is clear claims, useful answers and real proof.

Step 6

Monitor what AI engines actually say

You cannot improve what you do not check. Run the same prompt set regularly, record answers, save cited sources and track whether your brand appears, disappears or is described incorrectly.

Run an AI visibility check for [BRAND]. Use these questions: [PASTE QUESTION MAP] For each question, report: – Does [BRAND] appear? – Position in answer – Sentiment / framing – Competitors named – Sources cited – Missing or wrong facts – Suggested content update Output table: Question | Appears Y/N | Framing | Competitors | Sources | Fix needed | Priority

Execution

The 14-day AI visibility sprint

The old version used a 7-day burst. That is still useful, but the better approach is a 14-day sprint that includes baseline, content creation, proof, distribution and monitoring.

Days	Action	Output
1-2	Run AI visibility recon across answer engines.	Baseline and gaps list.
3-4	Create question map and prioritise weak answers.	Question backlog.
5-7	Publish one citation-ready answer asset.	Canonical page with proof and FAQ.
8-9	Build supporting evidence and internal links.	About, author, proof and related pages connected.
10-12	Distribute through LinkedIn, email, partner mentions and relevant communities.	Freshness and third-party signals.
13-14	Rerun AI checks and record changes.	Visibility delta and next fixes.

Templates

Copy-paste prompts

Competitor citation scan

Compare AI visibility for these brands: [BRAND 1] [BRAND 2] [BRAND 3] Use this market: [INDUSTRY / LOCATION / AUDIENCE] Answer: 1. Which brands are recommended most often? 2. What language is used to describe each brand? 3. Which sources are cited? 4. Which brand appears most trustworthy and why? 5. What content gaps should [MY BRAND] fill? Output: Brand | Common framing | Cited sources | Strength | Weakness | Opportunity

Citation-ready page generator

Create a citation-ready article for: Question: [QUESTION] Brand/entity: [BRAND] Audience: [AUDIENCE] Evidence available: [PASTE PROOF] Requirements: – Answer in the first 80 words – Use clear headings – Avoid hype – Include evidence and limitations – Include FAQ – Include internal link suggestions – Include 3 quotable lines – Include quality gate checklist Output as a structured draft.

Publishing target builder

Build a publishing target list for [INDUSTRY]. Find surface types that would strengthen AI visibility: – Industry publications – Founder or expert profiles – Directories – Partner sites – Technical docs or GitHub if relevant – Community discussions Output: Surface | Why it matters | Content angle | Difficulty | Priority | Next action

Operations

Tracking worksheet

Keep this simple. The point is not a huge dashboard. The point is a repeatable loop.

Question	Target page	Engine checked	Brand appears?	Sources cited	Issue	Next action
Best [service] for [audience]	[URL]	ChatGPT / Perplexity / Claude / Google / Bing	Y/N	[Sources]	[Wrong or missing fact]	[Fix]

FAQ

Common questions

Is this SEO?

It overlaps with SEO, but it is not the same. SEO is still about pages, rankings, technical health and search demand. AI visibility is about whether answer engines can understand, verify, summarise and cite you inside generated answers.

Is GEO the same as AEO?

They are related. AEO usually means answer engine optimisation. GEO usually means generative engine optimisation. In practice, both point to the same shift: building content and signals that make you useful to AI-generated answers.

How fast does it work?

Sometimes you can see movement quickly, especially in low-competition niches. In competitive markets, expect this to be a repeated content, proof and monitoring loop rather than a one-off campaign.

What matters most?

Clear entity identity, direct answers, credible proof, trusted surfaces, consistent descriptions and regular monitoring.