Quick Answer: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are often used interchangeably. Both refer to optimizing how your content shows up in AI-generated answers. AEO typically focuses on direct answers in search (like snippets or voice), while GEO focuses on citations and representation in AI tools like ChatGPT, Gemini, or Perplexity. But the core goal is the same: visibility in AI answers, not just rankings.
What matters isn’t the acronym, it’s the output. Whether your content appears as a snippet in a Google AI Overview or gets quoted in a Perplexity answer, the goal is still brand visibility. Most SEOs working on these surfaces use the same playbook and simply adapt their tactics depending on the engine.
The takeaway for most teams? Don’t get stuck in the label debate. Treat AEO and GEO as lenses, not silos, and focus on how AI platforms select, trust, and reuse content.
Introduction: The Acronym Confusion Clients Are Feeling
AI is changing how people search. Instead of scrolling through 10 blue links, users now get instant answers, pulled from websites, synthesized by large language models, and delivered across platforms like Google, Bing, and ChatGPT.
As a result, marketers and SEOs are scrambling to name this shift. Some call it AEO, others call it GEO, and some layer on terms like LLMO or AI SEO. It’s no surprise that many teams, and clients, feel confused.
So here’s our stance as an AEO agency:
AEO and GEO are overlapping labels. They describe the same fundamental goal: getting your brand featured inside AI-generated answers, whether that’s through direct extraction or generative synthesis.
In this guide, we’ll explain:
- Why some experts treat AEO and GEO as the same
- Where small differences actually exist
- How to explain it cleanly to clients
- What to measure, regardless of the acronym
This isn’t a matter of redefining SEO, it’s about evolving it. Clients want outcomes: visibility, traffic, authority, and conversions. Whether that comes from AI answers or search snippets doesn’t change the fundamentals, it just changes how you track and report progress.
The naming debate is largely academic. The real challenge is staying ahead of how AI systems decide what content to trust, cite, or surface, and ensuring your brand is part of that conversation.
Why AEO and GEO Are Often Used Interchangeably
Let’s start with why this overlap exists, and why it’s actually helpful.
1. They’re naming the same shift
Both AEO and GEO were coined to describe the transition away from traditional search visibility and toward answer-first visibility. Whether the answer comes from a Google snippet or an LLM-generated paragraph, the underlying strategy is the same: make your content the source AI systems choose.
This shift reflects user behavior. People are no longer clicking through to ten sites to get an answer, they’re trusting AI to give it to them directly. AEO and GEO are both trying to solve for this same moment: when the AI generates a response, will your brand be part of it?
Even the underlying risks are the same. You’re dealing with opaque AI systems, evolving output formats, and non-guaranteed citations. Both AEO and GEO work to reduce the chance of your content being ignored or misrepresented in those systems.
2. Even top SEOs group them together
Ahrefs groups AEO, GEO, and LLMO under the same umbrella. Their stance? These are all “different names for the same idea,” and the tactics involved still come from core SEO principles: topical authority, trust signals, and high-quality content.
ProFound, an agency working in this space, presents GEO as a newer label for what many already called AEO.
This grouping avoids fragmentation. Instead of splitting teams into competing disciplines, it helps unify strategy under one umbrella: visibility in AI answers. The naming might shift, but the optimization methods, building authority, using structured content, earning links, remain consistent.
It’s also better for resourcing. If you treat GEO and AEO as one strategy, you don’t need two teams, two budgets, or two roadmaps. You just need one clear plan with adapted outputs and measurements.
3. Social chatter treats them as synonyms
In Reddit threads and LinkedIn discussions, marketers often roll their eyes at the acronym overload. The common sentiment? “It’s all SEO, just adapted for AI.” That skepticism reflects how many professionals work: they’re focused on execution, not branding trends.
This mirrors what happens during any SEO evolution. We’ve seen it before with terms like ASO, YouTube SEO, or “semantic SEO”, the tactics often overlap, and practitioners gravitate toward what works rather than what it’s called.
And in a client setting, simplicity wins. If a CMO just wants to know how their brand will show up in ChatGPT answers, you don’t need to debate acronyms. You need to explain the output, show the impact, and outline how you’ll measure progress.
The Slight Difference (If You Want to Use It)
While the tactics are shared, some teams find it helpful to draw a loose distinction between where your content appears and how it’s used.
| Lens | Surface Type | Optimization Focus | Example Outputs |
|---|---|---|---|
| AEO | Direct answer surfaces | Structure + clarity | Snippets, AI Overviews, voice search |
| GEO | Generative content | Entity consistency + citation | ChatGPT, Gemini, Perplexity |
Here’s the nuance:
AEO emphasizes answer extraction, how clearly your content answers a question so it can be lifted into a feature like a Google AI Overview.
GEO emphasizes inclusion and citation, whether your brand is used, referenced, or quoted inside a synthesized generative response.
This is useful for internal clarity or reporting breakdowns. But in real-world campaigns, the work overlaps heavily.
You can also use this difference to drive roadmap priorities. For example, if you’re targeting AI Overviews, you’ll focus on structure, FAQ blocks, and schema. If you’re chasing ChatGPT citations, you’ll spend more time on entity linking, off-page corroboration, and prompt coverage.
The overlap also shows up in how tools measure success. Many platforms, like Perplexity or Bing Copilot, pull from both structured content and authoritative sources, meaning AEO and GEO signals are often both needed for inclusion.
How We Frame It for Clients
AEO and GEO are often just different names for the same playbook: making sure your content shows up where AI writes the answer.
AEO and GEO are overlapping labels, and the industry uses them inconsistently. In practice, both describe optimizing brand visibility inside AI-generated answers. Where people draw a line, it’s usually about emphasis: AEO leans toward direct answer surfaces, while GEO leans toward being included or cited in generative responses. We treat them as one discipline with two measurement lenses.
This framing simplifies conversations and keeps the focus on outcomes, not acronyms.
We focus the conversation on surfaces and KPIs. Instead of asking, “Are we doing AEO or GEO?”, we ask, “Which AI systems matter for your audience, and how do they pull content?” This keeps things practical and aligned with business outcomes.
And it’s easier to onboard new stakeholders. Whether you’re talking to content, brand, or product teams, using one shared vocabulary around “AI answer visibility” helps unite efforts across disciplines without getting bogged down in naming.
Behavior Proves the Difference in Output, Not Strategy
So if the tactics are shared, does the output behavior differ? Yes, and the best proof comes from real data.
Ahrefs ran large-scale studies comparing how different AI platforms select their sources:
| Surface | % of Citations from Top 10 Google Results | Retrieval Behavior |
|---|---|---|
| Google AI Overviews | 76% | Pulls directly from high-ranking pages |
| ChatGPT, Perplexity, Gemini | 12% | Uses broader prompt expansion logic |
What This Means:
Google’s AI Overviews behave like search. If you rank, you’re more likely to be cited.
AI assistants behave like information synthesizers. They pull from a wider web of sources, many of which aren’t in the top 10.
The implication? Ranking matters more for AEO-style visibility. Citation ecosystems matter more for GEO-style visibility.
This split reinforces the need for dual visibility strategies. Google’s AI Overviews still inherit traditional ranking mechanics, meaning classic SEO signals carry over. Meanwhile, LLMs like Gemini or Perplexity rely more on semantic associations and widespread reputation. You can’t brute-force your way into those citations by simply ranking, your content must be recognized as authoritative across multiple data points.
It also affects how you monitor performance. A strong showing in Overviews doesn’t guarantee inclusion in generative responses, and vice versa. That’s why tracking AI performance needs to expand beyond search rank and into coverage, citations, and representation across engines.
So… What Do You Actually Optimize?
Whether you call it AEO or GEO, the optimization pillars are the same, only the emphasis shifts slightly depending on the output surface.
For AEO Surfaces (Snippets, Overviews, Voice)
Focus on:
- Concise definitions and answers
- Structured content (FAQs, tables, lists)
- Schema markup and clean HTML
- Clarity and relevance above all
The goal is clarity. AEO success depends on whether your content can be easily extracted and displayed as a direct answer. That means stripping fluff, tightening copy, and formatting pages to highlight answers, not bury them.
You’re also working within Google’s systems. This gives you more transparency, more tools (like Search Console), and more predictable behavior. If you already rank well, even small formatting changes can help you appear in Overviews, without needing to rework entire sections.
For GEO Surfaces (ChatGPT, Gemini, Perplexity)
Focus on:
- Consistent entity naming (across site + offsite)
- Strong off-page citation signals
- Semantic alignment across content clusters
- Corroboration from external sources
The goal is trust. GEO success is about whether language models consider your content accurate, relevant, and credible enough to include, even when it’s not directly quoted. That’s why entity alignment, source corroboration, and clear topical authority matter so much.
You’re playing in a less structured system. Unlike Google Search, LLMs use varied data sources and operate more like reasoning engines. Your strategy here is about building a digital footprint, so when AI goes looking for credible inputs, your brand is in the mix.
But you don’t choose one or the other.
In practice, optimizing for one strengthens the other. That’s why we recommend treating this as one visibility strategy with layered measurement.
The “Visibility Stack” We Recommend
Here’s the full-stack model we use in client work to avoid overcomplicating things:
| Layer | What We Focus On |
|---|---|
| SEO Foundation | Crawlability, site structure, topical authority |
| AEO Layer | Answer formatting, snippet readiness, schema |
| GEO Layer | Entity consistency, external citations, prompt coverage |
| Measurement | Answer share, citation share, brand accuracy, outcomes |
This framework keeps strategy actionable. By separating concerns into layers, you avoid “SEO vs AI” confusion and instead build a stack where each level supports the others. You can scope projects, assign ownership, and report impact without debating terminology.
It also scales across platforms. Whether you’re optimizing for Google, Gemini, or Perplexity, this model holds. The key is tailoring outputs to how each surface consumes and ranks content, without reinventing the core of your SEO program.
And most importantly, it’s measurable. Each layer supports KPIs you can track, improve, and report on, giving clients visibility into what’s working and where to double down.
What to Measure Instead of Rankings
Since AI answers don’t always link out, and may not be trackable by rank, we use a broader set of KPIs to measure success.
| Metric | Description | Used For |
|---|---|---|
| Answer Share | % of tracked prompts where your brand is the direct answer | AEO |
| Citation Share | % of AI responses that include your site as a source | GEO |
| Prompt Coverage | % of intent-aligned queries that surface your content | AEO + GEO |
| Representation Quality | Is your brand described accurately in AI outputs? | GEO |
| Downstream Outcomes | Brand search lift, conversions, demo requests | AEO + GEO |
You’re not just measuring presence, you’re measuring influence. Being cited is one thing. Being described accurately and persuasively in the AI’s response? That’s a bigger win, and often harder to achieve.
These metrics also change your reporting cadence. Unlike traditional SEO, where rankings update daily or weekly, AI answer inclusion may fluctuate based on query rewrites, model updates, or prompt variations. That’s why we encourage monthly prompt sets, tracked AI screenshots, and entity audits.
And it creates new dashboards for clients. Visibility in AI responses isn’t obvious unless you show it. That’s why we build prompt libraries and test output snapshots, so clients can see how their brand appears in real AI answers, not just traffic charts.
What Platforms Say About This Shift
To avoid vague claims, we refer directly to how AI platforms describe their own systems:
Google (AEO-aligned)
- Treats AI Overviews as part of Search, not separate.
- Recommends using structured, factual content to support inclusion.
- Has formal documentation for AI features.
This gives AEO a clearer playbook. You can follow Google’s published guidelines, use tools like Schema Validator, and work within familiar SEO frameworks. AI Overviews are still very much tied to your ranking, and that’s a win for teams with solid SEO infrastructure.
Perplexity (GEO-aligned)
- Emphasizes citation-based responses and follow-up context retention.
- Cites a wide range of sources, often beyond the top 10.
Perplexity rewards source clarity. If your content explains a concept clearly, is easy to reference, and has consistent language, you’re more likely to be cited. This is GEO in action: content as input, not just destination.
Microsoft Copilot + Bing
- Draws from Bing’s index but uses LLM synthesis.
- Relevance isn’t always tied to ranking, but to prompt expansion.
This creates hybrid behavior. Copilot surfaces combine Bing search results with generative AI outputs, meaning GEO-style and AEO-style tactics can both influence inclusion. It also means you’re optimizing across both retrieval and reasoning, which makes the case for a unified approach even stronger.
Different tools behave differently, which supports using both AEO and GEO perspectives inside one strategy.
Final Thoughts: One Discipline, Two Lenses
Here’s the bottom line for any brand trying to win visibility in AI results:
AEO and GEO aren’t competing.
They’re two ways of describing the same evolution: content must now serve AI-generated answers, not just search result rankings.
Whether you’re trying to win snippets or citations, the foundations are shared: SEO, clarity, trust, consistency.
If your agency or team is debating acronyms, you’re asking the wrong question. The right one is:
“Is our content being selected, cited, and used by AI systems, and how do we measure that?”
That’s the real strategy.
That’s what clients care about.
And that’s what AEO + GEO, together, are here to solve.
In short? Build once. Measure across both lenses. Report in outcomes.
The rest is just naming.