How to Do AEO (Answer Engine Optimization): A Step-by-Step Playbook to Get Cited

Your analytics can't tell you which prompts mention you. That doesn't stop those prompts from shaping what people buy.

Here's what changed: when an AI summary appears in Google results, users click a traditional link only 8% of the time compared to 15% without one. Session-ending behavior jumps from 16% to 26%. The click is collapsing. The answer is becoming the destination.

If you've already shipped "best practices" SEO and still don't appear when someone asks ChatGPT about your category, you're not alone. The problem isn't your content quality. It's that you're optimizing for a scoreboard that no longer reflects how people find answers.

AEO isn't a checklist. It's a measurement loop. This guide gives you a practical 30-day playbook: define your scoreboard, build extractable pages, engineer corroboration beyond your site, and run a weekly cadence that compounds.

Understanding AEO is table stakes. The hard part is building the footprint and measurement loop that gets you cited when AI answers the questions that drive revenue.

Here's what you need.

Check if your brand appears in ChatGPT, Perplexity, and Google AI Overviews →

What You Need Before Starting AEO

Before diving into the steps, make sure you have access to:

Google Analytics + Search Console — You'll need baseline organic data to compare against AI referral signals
A list of 10-20 revenue-driving queries — The prompts and questions your buyers actually ask before purchasing
Content edit rights — The ability to publish and update pages without a 6-week approval cycle
Basic schema capability — Someone who can implement FAQPage and HowTo markup (or a CMS that supports it)
One SME on call — A subject-matter expert for edge cases and fact-checking

The good news: Google's documentation confirms there are "no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary." You don't need proprietary markup. You need a system to be selected.

The baseline is normal SEO fundamentals. The gap is everything else this guide covers.

Step 1: Define Your AEO Scoreboard (Mentions, Citations, and Controlled Prompt Tests)

Traditional SEO gives you rankings. AEO needs a different scoreboard because you're measuring something different: whether AI systems cite you when answering questions.

Your AEO scorecard has three layers:

Layer 1: Mentions and citations by engine

For a fixed set of prompts, track whether ChatGPT, Perplexity, Google AI Overviews, and Claude mention or cite your brand. This isn't vanity. It's the primary signal that your content is being selected.

Layer 2: Referral and engagement signals

AI referral traffic grew 357% year-over-year to reach 1.13 billion visits to top websites in June 2025. Track referrers from chat.openai.com, perplexity.ai, and Google's AI surfaces. Compare session quality (pages per visit, time on site, conversion rate) against traditional organic.

Layer 3: Page-level "selected as source" checks

Which of your URLs actually get cited? Track not just whether you're mentioned, but which pages models pull from. This tells you where to invest.

As Krishna Madhavan from Microsoft Advertising put it: "In today's world of AI search, it's not just about being found, it's about being selected."

The baseline is already zero-click for most searches. SparkToro found that 58.5% of U.S. Google searches end without a click. If the web is increasingly zero-click, visibility inside answers is the compounding asset.

For a comparison of tools that help with this measurement, see our GEO tools compared guide.

Step 2: Build a Prompt Set That Maps to Real Intent (and Rerun It Weekly)

The most common complaint from practitioners is prompt-level uncertainty: "How do you know when ChatGPT is mentioning your brand? Specifically what queries."

You don't need perfect query data to start. You need repeatability.

Build a prompt library of 10-20 prompt families organized by intent:

Intent Type	Example Prompts
Best-in-class	"Best [your category] for [use case]"
Comparison	"[Your brand] vs [competitor]"
Pricing	"How much does [your category] cost?"
Alternatives	"[Competitor] alternatives"
How-to	"How to [problem your product solves]"
What is	"What is [your product category]?"
Troubleshooting	"[Category] not working"
Compliance/risk	"Is [your category] safe for [use case]?"

Run these prompts across ChatGPT, Perplexity, Claude, and Google AI Overviews weekly. Store the responses and note which sources get cited.

The goal isn't comprehensive coverage. It's a repeatable baseline you can compare against after you ship changes.

You don't need perfect attribution to act. You need a system that turns "we're invisible" into "we're invisible on these 8 prompts, let's fix one."

Step 3: Make Your Pages Extractable (Answer-First Structure + Entity Clarity + Schema)

This is where most AEO advice stops: the on-page checklist. Here's what actually matters for selection.

Direct answer in the first 100 words

AI models don't read your whole page and then decide. They extract. If the answer to the reader's question isn't in the first paragraph, you lose to competitors who front-load it.

Question-shaped H2s with short "answer paragraphs"

Structure your content so each H2 is a question (or close to it) followed by a 2-3 sentence direct answer, then elaboration. This matches how models parse and quote.

Explicit entity definitions

Define your key terms clearly. Don't assume the model knows what "AEO" means in your context. Use the format: "[Term] is [clear definition]." This helps models extract and cite accurately.

"Best for" summaries above tables

Practitioners report that AI often misses context in tables. Add a quick summary above any comparison: "Best for X: [Option A]. Best for Y: [Option B]."

Schema where it matches intent

FAQPage for question-driven content. HowTo for step-by-step guides. But schema without clarity is useless. As John Mueller noted: "Focus on your visitors and provide them with unique, satisfying content."

Microsoft's guidance on AI search describes a "parsing" model where pages are broken into smaller pieces for selection. Your job is to make each piece standalone and quotable.

Extraction is table stakes. Unclear pages get ignored even if they rank.

Step 4: Build a Library of Citable Blocks (Stats, Quotes, FAQs, Comparisons)

This is where you turn SME judgment into reusable assets that models can cite repeatedly.

Create 10-15 "proof blocks"

Each proof block is a self-contained unit with a clear claim, supporting evidence, and a source. Example: "According to [Source], [specific stat]. This means [implication]."

Store these in a central document. Reuse them across pages. Give each a permanent URL (even if it's an anchor link to a page section).

Build 20-40 FAQs from real objections

Pull questions from sales calls, support tickets, and community threads. These are the questions people actually ask, not the ones you wish they asked.

Create 5-10 comparison snippets

For every major competitor or alternative, build a comparison block with a "best for" summary. This makes your content quotable for "[Brand] vs [Competitor]" prompts.

Research suggests that GEO strategies including citations, statistics, and quotable content can boost visibility by up to 40% in generative engine responses. The mechanism: models cite what is clean, specific, and corroborated.

The goal isn't to bloat every page. It's to have a library of citable assets you can deploy where they fit naturally.

The operational reality: Understanding AEO structure is table stakes. The execution—tracking visibility across engines, engineering citable blocks at scale, maintaining a library that stays fresh—is where most teams get stuck. That's the Track → Engineer → Leverage → Own system that makes this repeatable.

Step 5: Engineer Off-Site Corroboration (Lists, Reviews, Directories, Forums)

Here's what most AEO guides skip: the work that happens outside your website.

AI models don't just read your site. They triangulate. If your brand appears on authoritative lists, in reviews, across directories, and in community discussions, models have multiple signals that you're a real player in the space.

Build a shortlist of 10-20 third-party targets

Target Type	What to Look For	Why It Matters
"Best X" listicles	High-DA sites with comparison content	Gets you cited on "[Category] alternatives" prompts
Review platforms	G2, Capterra, Trustpilot, industry-specific	Third-party validation models can trust
Directories	Industry associations, niche databases	Entity corroboration
Communities	Reddit, Quora, industry forums	Real practitioner mentions

Map distribution to prompt families

Your "best X" prompts need you to appear on "best X" lists. Your "vs [competitor]" prompts need third-party comparisons that include you. Match your off-site work to the prompts you're tracking.

AI referrals are growing fast. Similarweb found that AI referrals were up 357% year-over-year, with ChatGPT accounting for more than 80% of AI referrals to top domains.

Third-party corroboration reduces "is this real?" uncertainty. When models see your brand mentioned consistently across trusted sources, they're more likely to cite you.

For the full framework on building presence across channels, see our definitive guide to GEO.

Step 6: Run the AEO Operating Cadence (Measure → Ship → Distribute → Refresh)

This is where AEO becomes a system instead of a campaign.

Weekly scorecard review (30 minutes)

Run your prompt set. Update your mentions/citations tracker. Note which prompts improved, which stayed flat, and which competitors gained.

Build a "citation gap" backlog

For every prompt where you're not cited but should be, create a backlog item. Prioritize by: (1) revenue impact of the prompt, (2) difficulty of fixing, (3) competitor vulnerability.

Ship one improvement per week minimum

Maybe it's adding a proof block to an existing page. Maybe it's restructuring an H2 to be answer-first. Maybe it's getting on one more list. The cadence matters more than the size of each change.

Refresh schedule for close-but-not-cited pages

Pages that rank well but don't get cited often have a freshness or clarity problem. Flag them for quarterly refresh. One dataset suggests 95% of ChatGPT citations were from content less than 10 months old (treat this as directional, not definitive).

Reporting template for stakeholders

Build a simple dashboard: prompts tracked, citation rate by engine, referral traffic from AI platforms, week-over-week changes. This replaces "we're doing AEO" with "here's what changed."

The frustration practitioners express about tools is real: "Every tool tracks AI visibility... Are there any tools that actually help you perform the optimization?"

The answer is a process, not a dashboard. Tracking becomes useful when it feeds a backlog you actually ship.

Common Mistakes to Avoid

"We added schema and waited."

Schema helps, but it's not a magic switch. Without answer-first content and entity clarity, schema just makes poorly structured pages easier to ignore.

"We optimized only on-site and ignored corroboration."

Your domain is maybe 10% of what models see when they decide who to cite. The rest is what the web says about you.

"We measured traffic only (and couldn't explain impact)."

AI traffic is confusing. One practitioner put it: "ChatGPT is clearly sending visits but analytics shows nothing." Track mentions and citations directly, not just referrals.

"We treated this as a new acronym instead of a workflow."

AEO vs GEO vs AI SEO—the taxonomy doesn't matter. What matters is: are you running a measurement loop that turns visibility gaps into shipped improvements?

Most failures are measurement failures, not content failures. You can't improve what you can't see.

How Long Does AEO Take? (Realistic Timeline)

Week 1: Baseline + prompt set + scorecard

Define your 10-20 prompt families. Run them across engines. Build your first scorecard. You'll know where you stand.

Weeks 2-3: Ship extractable pages + proof blocks

Update your top 5-10 pages for answer-first structure. Build your initial library of citable blocks (FAQs, comparisons, stats).

Weeks 3-4: Distribution + refresh cycle

Target 3-5 third-party placements (lists, reviews, communities). Start your weekly refresh cadence.

According to Semrush, AI search visitors convert at 4.4x the rate of traditional organic visitors. Early signals often show up before big traffic numbers. Track engagement quality, not just volume.

You're buying a system. The first 30 days create the foundation. The next 90 compound it.

Putting It All Together: A 30-Day AEO Plan

Here's the week-by-week playbook:

Week 1: Foundation

Build your prompt library (10-20 families by intent type)
Run baseline prompt tests across ChatGPT, Perplexity, Claude, Google AI
Set up your AEO scorecard (mentions, citations, referrals)
Identify your top 10 pages by revenue potential

Week 2: On-Page Extraction

Audit top 10 pages for answer-first structure
Add direct answers to first 100 words where missing
Create entity definition boxes for key terms
Add "best for" summaries above comparison tables

Week 3: Citable Assets

Build 10 proof blocks from existing research/data
Create 20 FAQs from sales objections and support questions
Implement FAQPage schema on top FAQ content
Draft 5 comparison snippets for competitor prompts

Week 4: Off-Site + Cadence

Identify 10 target third-party placements (lists, reviews, directories)
Submit to 3-5 high-priority targets
Set up weekly scorecard review (30 min recurring)
Run second prompt test and compare to baseline

What you own at the end of 30 days:

A prompt library you can rerun indefinitely
A scorecard that shows citation progress
A publishing checklist for new content
An off-site distribution plan
A weekly cadence that compounds

Remember what Pew Research found: when AI summaries appear, clicks drop by half. The brands that build this system now will own the answers. The brands that wait will wonder where their traffic went.

Ready to see where you're invisible?

We'll run your key queries through ChatGPT, Perplexity, and Google AI Overviews and show you exactly where competitors get cited and you don't. Takes 30 minutes.

Get your AI visibility audit →

Not ready for an audit? See how AEO and GEO compare →

Frequently Asked Questions

How do you know when ChatGPT is mentioning your brand? Specifically what queries

Short answer: you don't get query-level data the way Search Console provides it. What you do instead is build a controlled prompt set and run it regularly. Create 10-20 prompts that map to your buying journey ("best [category]", "[you] vs [competitor]", "how to [problem you solve]") and test them weekly across ChatGPT, Perplexity, and Claude. Store the responses. Track which sources get cited. This gives you a repeatable baseline even without query data.

The prompts you test become your coverage map. When you ship changes, you can see if citation behavior shifts.

What's the best AEO or GEO tracker?

Practitioners are frustrated because most tools track visibility but don't tell you what to do about it. The useful ones let you: (1) monitor mentions/citations across multiple engines, (2) compare your citation rate to competitors, and (3) export data you can act on.

For a detailed breakdown, see our GEO tools compared guide. The key isn't finding the perfect tool—it's building a process that turns tracking into a backlog of changes you ship.

Is AEO the same as GEO?

They overlap significantly. AEO (Answer Engine Optimization) focuses on getting cited in AI-generated answers. GEO (Generative Engine Optimization) is a broader term that includes ranking in AI search results, not just answers.

In practice, the tactics are similar: answer-first structure, entity clarity, proof density, and off-site corroboration. The distinction matters less than whether you're running a measurement loop. See AEO vs GEO explained for the full breakdown.

Why does my analytics show nothing from ChatGPT even though I see traffic?

This is common. AI referral attribution is messy. Some visits show as direct. Some show as referral but without detail. The platforms don't always pass referrer data cleanly.

Don't rely solely on referral tracking. Supplement it with controlled prompt tests (where you know you're cited) and engagement quality metrics (session duration, pages per visit, conversion rate) for traffic segments you can identify.

Can I just add schema and wait?

Schema helps models parse your content, but it doesn't make unclear content clear. FAQPage schema on vague answers is still vague answers.

Schema is an accelerant, not the engine. Use it when your underlying content is already answer-first, entity-clear, and corroborated. If you're not getting cited, the problem is usually structure or off-site presence—not missing schema.

How do I explain AEO impact to stakeholders?

Focus on three metrics: (1) citation rate on your tracked prompt set (are you showing up more often?), (2) AI referral traffic and its quality (session duration, conversion), (3) competitive position (are you gaining on competitors for key prompts?).

Build a simple monthly report: prompts tracked, citation changes, referral trends, specific wins ("we went from uncited to second source for '[category] alternatives'"). Stakeholders don't need to understand AEO mechanics. They need to see measurable progress.

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