The SEO industry has spent three years inventing new acronyms for the same underlying problem: AI systems are answering queries before users click anything. AEO, GEO, AI citation, generative search — these terms overlap enough to be genuinely confusing, and the difference matters for where you put your effort.
Here's the full disambiguation, then how each major engine actually works, then how to build a monitor.
The Three Terms
AEO — Answer Engine Optimization (coined ~2017) targets the pre-generative answer layer in traditional search: Google featured snippets, People Also Ask boxes, and voice answers via Alexa and Siri. The engine is still Google or Bing — just the zero-position result instead of rank #1. AEO is still relevant for queries that don't trigger AI Overviews.
GEO — Generative Engine Optimization (coined ~2023) targets AI systems that generate prose responses: Perplexity, ChatGPT Search, Gemini, Copilot. The optimization goal is being cited in a generated answer, not ranked in a list.
AI Citation is the outcome metric — did a specific engine actually reference your URL in its response? Citation rate per keyword, per engine, is what you're trying to move. It's the GEO equivalent of click rank.
Timeline of the answer layer
────────────────────────────────────────────────────
2017 AEO era begins
└─ Featured snippets, PAA, voice search
└─ Optimize for: direct answers, structured markup
2022 Generative engines launch (ChatGPT, Perplexity)
└─ New retrieval systems outside Google/Bing
2023 GEO coined, Google AI Overviews roll out
└─ Perplexity scales rapidly as a citation engine
2024+ AI citation becomes a trackable metric
└─ AEO ≠ GEO — different targets, different signals
────────────────────────────────────────────────────
They are not competing strategies — they're sequential layers on the same query path. A page can lose the AEO featured snippet to a competitor, still rank #2, and get cited in Perplexity. All three metrics are independent.
How Each Engine Cites
The retrieval and citation mechanism differs per engine. Treating them the same is a mistake.
Query: "best X for Y in 2026"
Perplexity
├─ Crawls via PerplexityBot (own index)
├─ Supplements with Bing results
├─ Generates prose answer
└─ Cites numbered sources inline: [1] [2] [3]
└─ Sources visible and clickable by the user
Gemini (Google)
├─ Grounded via Google Search
├─ Pulls from Google's organic index
├─ Your GSC rankings = your citation eligibility
└─ Sources shown in a "Grounding" panel
ChatGPT Search
├─ Crawls via GPTBot
├─ Primary index: Bing
├─ Inline footnotes in the generated answer
└─ Sources listed at the bottom of the response
Copilot (Microsoft)
└─ Bing-based, same citation pattern as ChatGPT Search
The practical consequence: if you rank in Google, Gemini is likely to cite you — it's a proxy of the organic index. If you rank in Bing, ChatGPT Search and Copilot follow. Perplexity is the most independent — it runs its own crawl and will surface smaller, newer sites that Bing or Google haven't fully valued yet. That makes Perplexity both the most interesting engine to track and the most actionable to influence.
What Perplexity Actually Favors
Perplexity pulls the most relevant excerpt from a page, not a summary of the whole thing. This shifts what matters:
- A direct answer in the first two sentences carries disproportionate weight
- Specific numbers and dates beat vague claims — "from $40/hour" beats "affordable"
- Freshness markers ("June 2026", "as of Q2 2026") increase citation likelihood for competitive queries
- Content buried after long introductions loses to a competitor with the same information higher on the page
Structured content also extracts cleanly — tables and numbered lists get pulled into citations more reliably than dense prose paragraphs.
How AEO and GEO Signals Overlap (and Where They Diverge)
Signal AEO GEO (Perplexity) GEO (Gemini)
──────────────────────────────────────────────────────────────────────
Direct answer up top ✅ High ✅ High ✅ High
Structured markup ✅ High ○ Medium ✅ High
Specific facts/numbers ✅ High ✅ High ✅ High
Freshness markers ○ Medium ✅ High ○ Medium
Backlink authority ✅ High ○ Low ✅ High
Google organic rank ✅ Required ○ Helpful ✅ Required
Bing rank ○ Helpful ○ Helpful ✗ Irrelevant
Content specificity ✅ High ✅ High ○ Medium
──────────────────────────────────────────────────────────────────────
The overlap is why improving content structure lifts all three simultaneously. The divergence is why a page can win Perplexity citations without being a Google authority — specificity beats domain rank in Perplexity's system in a way it doesn't in Gemini's.
Building a Citation Monitor
Manually checking whether Perplexity cites you across a keyword set doesn't scale past a few queries. The right approach: automate it with a weekly script that queries an API endpoint, parses citations, compares against a target domain, and logs the result.
A GEO Guard cloud function handles the Perplexity query layer — it submits the query and returns structured citation data:
def check_citation(query, domain):
resp = requests.post(
f"{FUNCTIONS_URL}/searchPerplexity",
json={"query": query, "targetDomain": domain},
timeout=30
)
d = resp.json()
return {
"cited": d.get("isCited", False), # URL in source list
"mentioned": d.get("isMentioned", False), # domain in prose only
"score": d.get("relevanceScore", 0), # 0–100 relevance
"sources": d.get("citations", [])[:3], # top cited URLs
}
Three states matter: cited (your URL appears in the source panel), mentioned (your domain name appears in the prose but isn't sourced), and not cited. Mentioned without cited is a partial win — you're in the training retrieval but not winning the citation slot.
Store results per keyword with a rolling 12-week history. The trend is the signal: a keyword that was cited last week and dropped this week means something displaced you — a fresher page, a more specific answer, a newly indexed competitor. That's the cue to update content, not rebuild it.
Weekly summary format
🤖 GEO Citation Report — Mon Jun 2, 2026
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
site.com — 11/14 cited
✅ "keyword cluster A" score: 90 ↑ (new this week)
✅ "keyword cluster B" score: 90
👀 "keyword cluster C" score: 60 ↓ (was cited)
❌ "keyword cluster D" score: 50
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Run it on a Monday cron before your weekly SEO digest so the data is already waiting when you review.
What the Pattern Looks Like in Practice
Across sites I've monitored, citation rate splits cleanly by query type:
- Specific destination or comparison queries cite reliably — Perplexity has fewer authoritative sources and will surface a well-structured page that directly answers the question
- Broad informational queries cite inconsistently — too many high-authority sources competing, specificity alone isn't enough to displace them
- Transactional queries ("buy", "near me", "price") rarely get generative answers at all — Perplexity routes these to direct links rather than prose, same as Google does
This is useful for prioritisation: don't spend GEO effort on queries where the AI engine just returns a map or a price list. Spend it on the specific informational queries where you already have structured, dated, factual content.
The Full Picture
AEO work ──► zero-position features in Google/Bing
└─ featured snippets, PAA, voice answers
GEO work ──► citations in generative engine responses
├─ Perplexity: independent crawl, rewards specificity
├─ Gemini: Google index proxy, rewards organic rank
└─ ChatGPT/Copilot: Bing proxy, rewards Bing rank
AI Citation ─► the metric proving either worked
└─ per keyword, per engine, tracked weekly
None of these replace the other. AEO still matters for queries that don't trigger generative answers. GEO matters for the ones that do. AI citation rate is the number that tells you which content is actually getting picked up versus being ignored.
The underlying content work is the same for all three: direct answers early, specific facts, clean structure, fresh dates. Get that right and you're optimizing for all of them simultaneously.
Originally published on tedagentic.com
