The AI Visibility Alphabet: GEO, LLM Optimization, and AEO Explained and When to Use Each
If you have heard “we need an AI strategy” more than once this month, you are not imagining it. Leaders are seeing AI answers show up everywhere, and they want your brand to be part of those answers.
The tricky part is what happens next. Most teams start with the things that are easiest to move quickly. They add FAQs, tweak blog intros, and test prompts to see what shows up. That work can help. It just works better when it follows a clear plan.
First, align on the terms. Then decide what you are optimizing for and why.
The shift that makes the acronyms matter
Traditional search is built around links. Even when SEO gets complicated, the mechanics stay pretty consistent: rank, earn the click, move the visitor to your site.
AI answers behave differently. A lot of the time, the destination is the answer itself. People read a summary, skim a comparison, get a recommendation, and move on. Sometimes you get a click. Often, you do not.
So if your only scoreboard is traffic, it can feel like the ground is shifting under you. Because it is.
A more useful set of outcomes looks like this:
Do you show up when the right questions get asked?
Are you described accurately?
Are you cited as a source, or just mentioned?
Do you get recommended when you actually fit?
That is why the alphabet soup exists. Each term points to a different problem, and each problem calls for different work. The labels are still settling, so the goal is less about picking a perfect acronym and more about choosing the outcome you are driving toward.
What GEO, LLM optimization, and AEO actually mean
GEO is the umbrella
GEO, generative engine optimization, is the broad strategy for showing up in AI answers across multiple surfaces, not just one platform.
Use GEO when the question is bigger than a content tweak:
Where do we show up today?
What do we want people to understand about us?
What seems to shape those answers?
How will we measure progress if clicks are only part of the story?
If you need one term that keeps leadership, SEO, content, and product marketing aligned, GEO does that job. Strong core SEO foundations still matter here, because discoverability and crawlability are what make everything else possible.
LLM optimization is about how you are understood
LLM optimization is narrower and more brand-sensitive. It focuses on how LLM-driven systems interpret your brand, summarize it, and position it next to competitors.
This becomes important when:
You are frequently compared to competitors
Your category is easy to flatten into something generic
The AI version of your positioning feels close, but not quite
Details are wrong, outdated, or inconsistent
In those situations, the work is less about creating more content and more about tightening the sources that define you, so you stop losing meaning in the summary. A helpful starting point is entity clarity: the consistent facts and associations these systems use to understand who you are across your site and the broader web.
AEO is the tactical lane
AEO (answer engine optimization) is the most tactical of the three.
AEO is about giving straightforward answers to real questions in a format that is easy to reuse. If your buyers ask the same questions every week, AEO is often the cleanest starting point.
It is also where teams build momentum fast, because you can ship improvements without turning the work into a significant initiative.
When to use each
If you want a simple decision rule, use this:
AEO is for owning a set of questions.
LLM optimization is for accuracy and preference.
GEO is for a complete, cross-functional program.
Here’s how that plays out.
Choose AEO when the goal is owning a set of questions
AEO works best when you focus on the questions that show up late in the funnel, where clarity actually affects the pipeline:
What does this do?
What is the difference between these approaches?
What does implementation look like?
Who is this not for?
What are the tradeoffs?
Clear answers help humans and machines. They are easier to quote, easier to summarize, and harder to misunderstand.
AEO also forces discipline. If you cannot answer a question cleanly, that is usually a positioning issue hiding inside a content issue.
Choose LLM optimization when perception and precision matter
If your team is hearing things like “prospects think we do X” or “we keep getting grouped with the wrong competitors,” that is an LLM optimization problem.
This is where minor inaccuracies create real friction. The summary may be technically correct, but it leaves out what you actually win on. It may overemphasize a feature you barely talk about. It could use language your team stopped using a year ago.
In those cases, prompt testing alone won’t solve the underlying issue. The work is about making your brand easier to describe accurately:
Use consistent category language across key pages
Make your differentiators explicit, not implied
Build a few strong source-of-truth assets that are easy to interpret and hard to misread
Use first-party inputs where you can, like internal research, customer surveys, or performance data that no one else can copy
Choose GEO when you need a program, not a patch
GEO is the right framing when the work spans:
Owned content and site structure
Brand positioning pages and product marketing assets
Technical foundations like crawlability and duplication
External sources that influence what gets pulled into answers
If the question is “what is our AI visibility strategy,” the answer is not “publish more FAQs.” The answer is a repeatable program with measurement, priorities, and a cadence, including decisions about how much ongoing prompt tracking is actually worth it for your category.
The misconception that wastes the most time
A lot of teams treat this like “SEO, but for prompts.”
Prompt testing matters. Keyword intent still matters. You should know what people ask and how they ask it.
But these systems behave differently from classic search:
Answers fluctuate
Context matters more than people expect
One prompt often turns into multiple sub-questions behind the scenes
The system is trying to answer first, and sending traffic is optional
So yes, test prompts. Just do not confuse the test harness with the strategy.
The baseline audit to run before you optimize anything
If you want this to be a real initiative and not a collection of random edits, start with a baseline. It takes a day. It saves weeks.
1) Capture what AI says about you right now
Pick a small set of prompts and save responses. Keep them consistent so you can repeat them monthly:
What is [brand]?
Who is [brand] for?
What does [brand] do best?
Compare [brand] vs [competitor]
What are the best alternatives to [brand]?
You’re looking for patterns, not perfection:
What is accurate
What is missing
What feels dated
What is confidently wrong
Which competitors show up alongside you
This is also where entity clarity shows up quickly. If basic facts about who you are, what you do, and what you are known for are inconsistent, everything downstream gets harder.
2) Separate mentions, citations, and recommendations
These are not interchangeable:
A mention means you showed up
A citation means you were used as a source
A recommendation means the system is steering someone toward you
Depending on your category, you might care most about one of these. The mistake is treating them like the same outcome.
3) Check referral traffic, then evaluate it like a channel
Look at the traffic, but do not get stuck on volume:
Which pages do visitors land on?
What do they do next?
Do they convert or assist conversions?
Does the behavior look high intent?
Small volume with strong intent is still meaningful. It also tells you where to focus first.
It also helps to track influenced metrics alongside referral traffic, like changes in branded search demand and direct traffic, since AI visibility can show up there before it shows up in click-through volume.
4) Identify what is shaping the answers
If your site is not being referenced, something else is shaping the narrative. Often, that’s community content, reviews, comparisons, and third-party explainers.
Do not panic about that. Use it as a map. Once you know which sources recur, you can decide what to reinforce, what to correct, and where you need to be more present. In many categories, off-site sources like Reddit, Quora, YouTube, and competitor comparison pages can have an outsized influence, which makes this closer to digital PR than traditional on-site optimization.
5) Tighten your source-of-truth content
Most brands do not need hundreds of new pages. They need a few pages that do the heavy lifting clearly:
A plain-language “what we do” page
Clear audience and use case pages
Capability pages that remove ambiguity
One or two comparison pages that answer the question directly
As you update these pages, structure content so it is easy to pull into multi-step answers. Clear section labels and “chunkable” explanations help systems assemble responses that reflect the full conversation behind a prompt.
A practical 30-day starting plan
Week 1: Run the baseline audit. Pick 3 to 5 prompt themes. List the most significant gaps.
Week 2: Tighten your source-of-truth pages. Fix clarity issues. Publish what is missing. Use first-party inputs where you can to make the content meaningfully different.
Week 3: Improve structure. Clean up page organization. Add schema where it genuinely helps interpretation, and align labels across key pages so your entity is easier to understand.
Week 4: Strengthen the external footprint. If third-party sources shape answers, show up where your buyers already look, and decide what level of ongoing prompt tracking is actually worth the effort.
Not sure where to start with AI visibility?
Want to turn AI visibility into something you can measure and improve? Kinetic can run the baseline audit, clarify where your brand is showing up, and prioritize the highest-impact moves for the next 30 days.
