Best practies for Answer Engine Optimization
Best Practices for Answer Engine Optimization (AEO)
A Q&A guide for the age of ChatGPT, Perplexity, Gemini & friends — with a practical workflow using GeoZ.ai
Before going deeper, it’s worth acknowledging three uncomfortable truths:
- AEO is early and messy. There’s no canonical playbook yet.
- Answer engines are closed systems. You can’t see all the “ranking factors” or logs.
- Most AEO tools (including mine) are evolving with the ecosystem, not fully solved oracles.
With that in mind, treat this guide as a practical way to think, not a list of magic hacks.
What is Answer Engine Optimization (AEO), in simple words?
Answer Engine Optimization (AEO) is the practice of making your brand, product, or content more likely to be used and mentioned when AI systems generate answers.
Instead of asking:
“How do I rank my page in Google’s 10 blue links?”
You start asking:
“When someone asks ChatGPT / Perplexity / Gemini a question that I care about, how do I become part of that answer?”
You’re not just optimizing for clicks anymore, you’re optimizing for answer inclusion:
- Being quoted or cited as a source
- Being named explicitly as a recommended product or brand
- Being summarized accurately in the model’s own words
If SEO was about getting seen in search results, AEO is about getting trusted inside the answer itself.
But here’s the critical bit:
You never fully control the answer. Models can hallucinate, misinterpret your positioning, or simply ignore you because of training data gaps. AEO is less “do X, get Y” and more “stack the odds in your favor” in a probabilistic system.
This is exactly the mindset GeoZ.ai is built around: tracking where and how your brand shows up inside AI answers so you can move from “invisible” to “part of the default answer set” — while accepting that no tool can guarantee you’ll be in every answer.
Once you start thinking this way, a natural next question appears:
If AEO is about answers, how is it really different from traditional SEO?
How is AEO different from SEO?
SEO mostly deals with documents, rankings, and clicks.
AEO deals with answers, reasoning, and citations.
Key differences at a glance
| Aspect | SEO (Old World) | AEO (Answer World) |
| ----------------- | ---------------------------------------- | ------------------------------------------------------- |
| Unit of focus | Web pages and keywords | Questions, prompts, and answer patterns |
| Decision maker | Search algorithms ranking URLs | Large language models deciding what to trust and cite |
| User behaviour | User scans links and chooses where to go| User reads a single composed answer |
| Main success KPI | Impressions, rank, CTR | Answer inclusion, narrative, and downstream action |
So, SEO asks:
“How do I move from position #7 to #3?”
AEO asks:
“How do I become the example, brand, or source this model keeps reusing?”
Critical thought: A lot of what’s sold today as “AEO” is just SEO with a new label. Models still rely heavily on web content, authority signals, and links. If your basic SEO is broken, chasing pure AEO “hacks” is like trying to optimize for Formula 1 while your car doesn’t even have wheels.
That’s why tools like GeoZ.ai focus on AI Answer Analytics for marketers — measuring how often and where AI engines surface your brand, not just how often Google shows your URL. It doesn’t replace fundamentals; it helps you see how the new layer (answers) behaves on top of them.
Once you see this shift, you naturally start to ask:
Why are answer engines (and not just search engines) suddenly so important for discovery?
Why are answer engines changing how people discover brands?
Answer engines compress the entire journey from question → research → summary into a single step.
Old vs new discovery journey
| Step | Old Discovery Journey (Search) | New Discovery Journey (Answers) |
| ---- | ---------------------------------------------------- | ---------------------------------------------------------------- |
| 1 | Search on Google | Ask Perplexity / ChatGPT / Gemini |
| 2 | Open 5–10 tabs | Read one consolidated answer |
| 3 | Skim, compare, and synthesize information | Maybe check a couple of citations |
| 4 | Decide and finally take action | Decide and act |
In this compressed journey:
- There’s less surface area for you to show up as “result #7”.
- The model itself becomes the editor and curator of which brands or ideas matter.
- The answer is often read once, trusted quickly, and then users move on.
Critical thought: this pattern is not universal. For some workflows (deep B2B evaluations, big-ticket medical or financial decisions), people still open multiple tabs, talk to sales, and do their own research. AEO matters most where users are happy to accept a single synthesized answer — but you shouldn’t assume that’s every industry, every persona, every ticket size.
Still, AI answers are increasingly becoming the first impression. Even if people later cross-check your website, their “mental default” can be set by Perplexity or ChatGPT in those first 30 seconds.
If answers are the new homepage, then the obvious next question is:
What does “visibility” even mean inside an answer?
What does “visibility” mean inside an AI-generated answer?
In AEO, visibility is not just “did my URL appear?” It’s more like a mix of mentions, citations, and how you’re framed.
Dimensions of visibility inside answers
| Dimension | What it means in practice |
| ----------------- | --------------------------------------------------------------------------- |
| Citation presence | Your URL is listed as a source / footnote / reference |
| Brand mentions | Your brand or product name is explicitly mentioned |
| Narrative quality | The description of what you do is accurate and clear |
| Comparative role | You’re positioned fairly in lists and comparisons vs competitors |
These are exactly the kinds of signals platforms like GeoZ.ai help you track across ChatGPT, Perplexity, and other answer engines — so you’re not guessing whether you’re visible inside the answer; you can see it.
Critical thought: you will never see the full internal reasoning graph of these models. You only see their surface behaviour (answers + citations). That means:
- Your metrics are proxies, not ground truth.
- Some answers might be influenced by your content even when you’re not cited.
- Some citations might be cosmetic while the “real” signal came from elsewhere.
AEO metrics are useful, but they live in a fuzzy layer between strict analytics and behavioural observation.
Once you understand what visibility looks like inside an answer, the next logical step is:
How do I figure out which questions (and answer contexts) I should be visible in?
How do I find the questions my audience is really asking?
You can’t optimize for answers if you don’t know the questions.
Here’s a practical way to discover them:
- Start from real customers
- Talk to sales, support, and success teams.
- Pull common questions from tickets, chats, and demo calls.
- These are often the same questions users will ask AI tools.
- Translate “search keywords” into “natural questions”
- Take important SEO keywords and rewrite them as full questions:
-
best payroll software for startups in US → What’s the best payroll software for early-stage startups in the US? - This is closer to how people actually talk to answer engines.
- Use AI tools themselves as research partners
- Ask: “What are the most common questions people might ask about [problem]?”
- Ask: “If someone is evaluating tools for [use case], what comparison questions might they ask?”
- Map questions to customer journey stages
Questions by customer journey stage
| Stage | Typical question patterns | Example |
| ------------ | --------------------------------------------------------- | ---------------------------------------------------------------- |
| Awareness | “What is…”, “Why does… matter?” | “What is answer engine optimization?” |
| Consideration| “What are the best tools for…”, “How do X and Y compare?”| “What are the best GEO tools for SaaS companies?” |
| Decision | “Which is better for [specific context]?” | “Which GEO tool is best for early-stage B2B startups?” |
Once you have a question map, you can feed those questions into a system like GeoZ.ai over time to see:
- Where you already appear in AI answers
- Where competitors dominate
- Where you’re completely missing
That turns a vague “we should do AEO” into a focused backlog of gaps to fix.
Critical thought: unlike traditional search, you don’t get a public “keyword planner” for ChatGPT or Perplexity. You are inferring demand from adjacent signals (SEO data, support conversations, your own prompts). Expect blind spots. The goal isn’t perfect coverage; it’s to cover the 10–20 questions that actually move your pipeline.
And once you know what to target, you’ll ask:
Now that I know what people ask, how should I structure my content so answer engines actually trust and reuse it?
How should I structure content so answer engines trust and reuse it?
Answer engines love content that is:
- Clear in intent
- Each page or section should have a primary question it answers.
- Don’t bury the lead — state the main question and answer early.
- Well-organized
- Use headings that match real questions (like this blog).
- Break content into short, logical sections: definitions, steps, pros/cons, examples.
- Evidence-backed
- Include numbers, examples, and references.
- Link to primary research, official docs, or reputable third-party sources.
- Context-rich, not keyword-stuffed
- Use natural language, related terms, and real phrasing your audience uses.
- LLMs are good at understanding meaning; you don’t need awkward keyword repetition.
- Explicit about who it’s for
- Make clear which segment you serve, for example:
- “for early-stage SaaS founders”
- “for mid-market HR teams”
- This helps answer engines know when you’re a good fit for a specific user query.
Critical thought: long-form content is not automatically better. Models often operate on chunks, truncate content, or weight intros and conclusions heavily. A 6,000-word wall of text with a vague intro may perform worse than a 1,200-word, tightly structured page with a crystal-clear opening.
Once your content is structurally strong, the next layer is:
Beyond structure and clarity, how do I signal authority and trust to an answer engine?
How do I signal trust and authority to answer engines?
LLMs are trained to prefer information that looks reliable, consistent, and corroborated.
You can help them by:
- Building strong, reference-worthy content
- Create definitive guides, FAQs, and “explainers” that others naturally link to.
- Keep them updated — outdated content is less likely to be reused.
- Earning mentions and links from credible sites
- Guest posts, podcasts, research collaborations, open data, case studies.
- The more reputable domains mention you, the more “background signal” you create.
- Being consistent across the web
- Align your positioning, tagline, and core messaging across your website, docs, social, and profiles.
- Inconsistent descriptions confuse models.
- Showing real-world proof
- Case studies, testimonials, user stories, or benchmark results.
- Models often summarize this kind of evidence when explaining why you’re credible.
Critical thought: authority is double-edged. If you publish low-quality or outdated content at scale, models can also learn the wrong things about you and repeat them for years. AEO is not just “create more content”; it’s “create fewer, higher-quality, more durable truths” that you’re happy to see repeated.
These authority signals matter, but answer engines also care about machine-readable clarity, not just human-readable trust.
That leads to the next question:
What technical steps can I take so answer engines can easily understand and reuse my content?
What technical best practices matter for AEO?
Think of this as “help the machines help you”.
Technical best practices overview
| Area | What to do | Why it helps |
| ----------------------- | --------------------------------------------------------------- | ---------------------------------------------------------------- |
| Structured data | Use schema / JSON-LD for products, FAQs, articles, org data | Gives models a clean, explicit representation of entities |
| Crawlability & speed | Render key content in HTML, keep pages fast and lightweight | Ensures content is easily accessible to crawlers and tools |
| Consistent naming | Use consistent names for brand, products, and key concepts | Reduces ambiguity when models connect mentions across the web |
| Documentation structure | Keep docs versioned, linked, and logically organized | Helps answer engines build accurate mental models for technical topics |
| Multimodal readiness | Use images, diagrams, short videos with meaningful alt/captions| Newer models can use non-text signals to refine answers |
Critical thought: technical tweaks are amplifiers, not substitutes for substance. Adding a schema tag to a weak page doesn’t make it “AEO-optimized”; it just makes a weak page easier to parse. Avoid cargo-cult behaviour (“we added FAQ schema, we’re done”) and tie every technical change back to an actual user question you want to dominate.
Once the technical pieces are in place, the next challenge appears:
If search rankings aren’t the main metric anymore, how do I actually measure AEO success?
How do I measure whether my AEO efforts are working?
AEO is still new, but you can start tracking a few concrete things.
What to measure for AEO
| Area | What to track | Example questions to ask |
| ------------------ | ----------------------------------------------------------------- | ------------------------------------------------------------- |
| Brand mentions | How often your brand is named in AI answers | “Which tools are best for [problem]?” |
| Citations | How often your domain appears in sources/citations | “Show references used to generate this answer.” |
| Narrative quality | Whether descriptions of your product are accurate and up-to-date | “Who is [brand] and what do they do?” |
| Competitive context| How you’re positioned vs others in lists/comparisons | “Compare [brand] with [competitor] for [use case].” |
| Downstream impact | Branded search, direct traffic, demo requests, sign-ups | “How many leads mention ChatGPT/Perplexity as discovery?” |
You can track some of this manually by asking questions in ChatGPT, Perplexity, Gemini, etc.
- Use specialized analytics tools
- This is where GeoZ.ai comes in: instead of manually asking a hundred prompts every week, you can systematically track where your brand shows up in AI answers, which competitors are stealing your visibility, and how that changes over time.
- Think of it as GA-style analytics, but for AI answers — built specifically for marketers who care about AEO.
- Run qualitative experiments
- Ask models: “Which companies are best for [niche problem you solve] and why?”
- See if you appear, and how you’re positioned relative to competitors.
Critical thought: AEO measurement is noisy:
- Models change frequently. An answer that included you yesterday might drop you today after a silent update.
- Different users, regions, or UI experiments may see different answers.
- It’s easy to confuse correlation with causation (“we changed headline → mentions went up”) when many hidden variables moved at the same time.
Treat your AEO metrics as signals to guide experimentation, not as precise scientific instruments.
Once you start measuring, the final piece of the puzzle shows up:
Given all this, how do I actually start implementing AEO without getting overwhelmed?
How do I get started with AEO without boiling the ocean?
Here’s a simple starting playbook:
- Pick 5–10 critical questions
- Questions where an AI answer that includes you would have real business impact.
- Think high-intent: “best X for Y”, “[category] for [segment]”, “alternatives to [competitor]”.
- Create or refine one strong, definitive resource for each
- A focused page, section, or guide that answers that question better than anyone else.
- Clear structure, real examples, and evidence.
- Align your broader web presence
- Make sure your homepage, docs, and key pages consistently describe who you are and what you do.
- Add structured data where relevant.
- Set up AI Answer Analytics
- Use a tool like GeoZ.ai to track how often your brand is appearing in AI answers today, which questions you already “own,” and which ones are blind spots.
- This turns AEO from a vague idea into a measurable, iterative process.
- Run regular answer audits
- Every few weeks, re-check your target questions across major answer engines.
- Use your GeoZ.ai dashboards plus occasional manual checks to see:
- Are mentions increasing?
- Are citations shifting?
- Is the narrative improving?
- Iterate like a scientist, not a gambler
- Change one thing at a time, then re-check how models respond.
- Treat answer engines as dynamic systems you learn from, not static black boxes you “hack” once.
Critical thought: don’t pivot your entire content or product strategy because of three screenshots from ChatGPT. AEO is a layer on top of your existing growth engine, not a replacement for product-market fit, sales, or brand.
If SEO was about reverse-engineering the ranking formula, AEO is about teaching the model who you are, what you do, and when you’re the right answer.
Tools like GeoZ.ai exist because marketers need visibility into that teaching process — without it, you’re just shouting into a black box and hoping the model remembers your name.
So the final, meta-question you can leave your team (or your CMO) with is:
In a world where answers are generated, not listed, is your brand just “another result”… or part of the answer itself?
The safest pattern: get your fundamentals right, pick a small set of critical questions, measure with tools like GeoZ.ai, and iterate slowly but deliberately.