How to use vector embeddings in AEO

A vector embedding is a numerical representation created by an embedding model. The model converts text into a list of numbers that can be compared with other vectors, helping a retrieval system find passages with similar meaning even when they use different words. Semantic retrieval is one tool AI systems can use to find source material; modern retrieval can also combine semantic search with keyword search and other relevance signals.

HubSpot’s State of AEO in 2026 reports that 58% of marketers say their businesses are already optimizing content for answer engines. Understanding the mechanics is useful even if you never build an embedding model yourself.

Franklin Rios, CEO of Next Net, used a simple analogy for large language models (LLMs) on the Found in AI podcast: “Vectorizing is embedding information into a data format. The reason we use a data format [is] because it’s the natural language of LLMs. They consume mathematics, they consume data.”

This isn’t engineering work you need to implement yourself. Instead, it changes how you think about generative engine optimization. The rest of this guide shows how passage-level retrieval, query fan-out, and AI visibility measurement translate into practical content decisions.

Table of Contents

• TL;DR: Vector Embeddings and AEO
• What are vector embeddings in AEO, and why do they matter?
• How to Use Vector Embeddings in AEO to Power Retrieval
• How to Use Vector Embeddings in AEO to Structure Citable Passages
• How to Use Vector Embeddings in AEO to Cover Query Fan Out
• How to Use Vector Embeddings in AEO to Measure AI Visibility
• What Marketers Should Know About Embedding Models for AEO
• Frequently Asked Questions About Vector Embeddings and AEO
• What Vector Embeddings Mean for Your AEO Strategy

TL;DR: Vector Embeddings in AEO

Vector embeddings represent the meaning of text as numbers so that retrieval systems can compare passages by semantic similarity rather than exact wording. For marketers, knowing how to use vector embeddings in answer engine optimization (AEO) means writing self-contained passages, keeping entity descriptions consistent, covering related questions created by query fan-out, and measuring which prompts and pages earn AI visibility.

The State of AEO in 2026

Dive into data about how marketers around the world are adapting to AEO and learn how to implement AEO yourself.

• Optimizing for answer endings.
• Making purchases based on brands discovered through answer engines.
• Improving brand citation from doubling down on AEO.
• And more!

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What are vector embeddings in AEO, and why do they matter?

A vector embedding is a numerical representation generated by an embedding model. The model converts a word, sentence, or passage into a vector — a list of numbers that lets a system compare that content with other vectors. In a semantic-search system, passages with similar meanings can end up closer together even when they use different wording.

David Kirkdorffer, a fractional marketer, joined me on Found in AI and gave the best plain-English version of this I’ve heard: “LLMs do word math. Certain words add up to mean certain things. Change the words, and we change the meaning.”

He used a thesaurus to explain it. If you’re writing and you’ve used the same word three times, you pull up synonyms. Some of the options on that list fit your sentence exactly. Others are technically synonyms and would wreck your meaning if you dropped them in. As Kirkdorffer put it, we intuitively go to the right word. The list is a chain, and moving along it shifts meaning by degrees until you’ve crossed into entirely different territory.

Kirkdorffer explained something most marketers experience without naming it: “We can say the same thing with different words and still have the same meaning. But if we change the words enough, we jump out of one kind of meaning into another kind of meaning.”

How Word Choice Moves Your Brand’s Meaning

Every time you rewrite your hero section in pursuit of language-market fit, you’re changing the language an embedding model has available to represent your brand.

Imagine a buyer asks an answer engine: “What’s the cheapest project management tool for a five-person agency?” Your page says: “Our Starter plan is built for small teams under 10 people who need budget-conscious project tracking.”

There is little exact wording overlap between those two passages, but their meanings overlap. A semantic retrieval system can identify that similarity even when a purely lexical comparison has fewer exact terms to work with.

That doesn’t mean keywords no longer matter. Modern retrieval systems can combine lexical and semantic matching. For AEO, the practical goal is to cover a topic clearly enough that both the words and the meaning connect your content with the questions buyers ask.

Sometimes the edits stay inside what Kirkdorffer calls a tight orbit of meaning: you’ve clarified the language, and your category is intact. Sometimes you move further than you realized and land somewhere adjacent. When that happens, your messaging can create inconsistent signals about what category your brand belongs to and which questions it is relevant to.

How to Use Vector Embeddings in AEO to Power Retrieval

Embeddings are a common way for retrieval systems to find semantically relevant content, but they aren’t the only retrieval mechanism. Modern retrieval-augmented systems can combine vector search with keyword search, filtering, and reranking. For marketers, the useful distinction is that an AI system may evaluate the relevance of a specific passage rather than treating an entire page as one indivisible unit.

Here’s what a vector-based retrieval process can look like from the inside.

What is retrieval-augmented generation in practice?

Retrieval-augmented generation (RAG) combines information retrieval with a generative model, allowing the model to use external material when producing an answer. A common vector-search RAG workflow looks like this:

1. The user’s question is converted into an embedding.
2. The system searches an index for semantically similar chunks of content. Depending on the architecture, it may also use keyword search, filters, or reranking.
3. The most relevant retrieved material is passed into the model’s context.
4. The model generates an answer using that material and, depending on the product, may cite the sources.

AI systems differ in how they implement retrieval. Rios described his experience this way: “80% of the journey is quite the same, and 20% is different from model to model.” Treat that as one practitioner’s estimate, not a universal platform specification. The practical lesson is to spend most of your effort on durable fundamentals instead of short-lived platform hacks.

Your content can compete in traditional search and for AI retrieval at the same time. Search rankings still depend on broader page and site signals, while retrieval can place additional emphasis on whether a particular passage is relevant and useful for a specific question.

How Passage-Level Relevance Beats Page-Level Relevance

A long page can be divided into multiple chunks for retrieval, which means different sections of the same page may be evaluated against different questions. The exact chunk size and number vary by system.

Rios described the processing approach behind his company’s system this way: “The chunking of every page, by the topic, the semantic, the relevance, the validity, and the trust score, it’s all in the mathematical model.”

Thinking about it in those terms changes some editing decisions:

• Make sections self-contained: A section should make sense when a reader encounters it without the paragraphs above it.
• Name the subject clearly: If a pronoun would make an excerpt ambiguous, restate the subject. “This process” can be replaced with “the vectorization process.”
• Keep one main idea per section: Mixing unrelated ideas can make the topic of a passage less specific.
• Lead with the answer: Give readers the direct answer before adding explanation, evidence, or nuance.

Pro tip: Copy any 150-word stretch out of the page, paste it into a blank document, and read it. Does it answer a question by itself? If it needs the surrounding page to make sense, revise it until the passage is clearer on its own.

For related terminology, see HubSpot’s artificial intelligence glossary.

How to Use Vector Embeddings in AEO to Structure Citable Passages

A citable passage answers a question clearly enough to make sense without requiring the surrounding page. As you adapt to the new era of search, start with writing patterns that make passages self-contained, then use on-page structure to make their subject and relationships explicit.

AEO Copy Patterns That Models Can Cite

Every copy pattern below does the same basic job: it makes a passage communicate something specific on its own terms. Here’s what I do when I write content for clients:

The Entity-First Statement

Write your brand into a subject-verb-object sentence like this: “[Brand] is a [category] for [audience] that [specific differentiator].”

Mine is “Cassie Clark is an AI search visibility consultant for scaling and enterprise brands.” You can find that positioning statement on my site, on my LinkedIn, in my podcast intros, on my social channels, and in most of my bylines.

Keep the core category, audience, and differentiator consistent across the surfaces your brand controls. That gives readers — and systems processing those sources — fewer conflicting descriptions to reconcile.

The Definition Block

Lead a section with a clean definition when the reader needs one.

For example, if you’re writing a post about query fan-out, start with an explicit definition: “Query fan-out is a technique that breaks one question into multiple related subqueries.”

A direct definition makes the answer explicit and keeps the passage useful when it appears outside the context of the full page.

Explicit Comparison and Tradeoff Language

Buyers ask comparison questions at decision time. If your content never states the tradeoffs, it may not answer those questions directly.

Add sentences like: “X is better for Y,” or “Z is better when you need W.”

Bounded, Labeled Sequences

Use numbered steps with named outcomes. “Step 3: Map the fan-out” communicates more on its own than “Next, we move on.”

Two things are happening there:

1. The label tells the reader what the step accomplishes, even when the step appears without its surrounding context.
2. The number establishes the step’s place in a defined sequence.

Explicit Temporal Markers

Add a date to claims that can become stale, such as pricing, feature sets, platform behavior, or time-sensitive statistics. A date provides the claim with necessary context; it does not, by itself, guarantee a boost in freshness or retrieval.

For example: “As of September 2026, HubSpot AEO refreshes AI visibility data daily across ChatGPT, Gemini, and Perplexity.”

For more on the broader relationship between AI and organic search, read AI and SEO: What AI Means for the Future of SEO.

Headings Phrased as a Question

When a question accurately describes what a section answers, consider using it as the heading. The heading gives both readers and automated systems a clear label for the material that follows.

The section still has to answer the question directly. A question heading followed by several paragraphs of setup is less useful than a clear heading followed by a concise answer.

Schema and On-Page Elements That Help Retrieval

Schema markup and embeddings do different jobs. Embeddings represent features of your content in a numerical form that can support semantic comparison. Structured data gives systems explicit, machine-readable information about a page and the entities described on it.

Use structured data accurately rather than treating a particular schema type as an AEO shortcut:

• Keep identity fields accurate: If you use Organization or Person structured data, make sure names, URLs, and identity references reflect the entity described on the page.
• Keep dates accurate: If you use Article structured data, make sure dateModified reflects a real content update.
• Match markup to the visible page: Don’t add FAQPage or HowTo markup simply because the page contains a few questions or steps. Google deprecated HowTo rich results and stopped showing FAQ rich results in 2026.
• Use semantic HTML: Real heading hierarchy, lists, and tables make the document structure explicit rather than relying solely on visual styling.
• Make critical content accessible without interaction: Google can render JavaScript, but rendering has limitations, and not every crawler executes JavaScript. Server-side or prerendered critical content reduces that dependency.

The exact weight major answer engines give structured data in retrieval is not publicly documented. From my testing, I’ve seen clean, structured data correlate with more accurate entity representations, but I treat that as an observed relationship, not as proof that the schema caused the citation.

The State of AEO in 2026

Dive into data about how marketers around the world are adapting to AEO and learn how to implement AEO yourself.

• Optimizing for answer endings.
• Making purchases based on brands discovered through answer engines.
• Improving brand citation from doubling down on AEO.
• And more!

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Download Free

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How to Use Vector Embeddings in AEO to Cover Query Fan Out

Some AI search systems expand a user’s question into related searches before generating an answer. Google documents this behavior in AI Mode as query fan-out: the system divides a question into subtopics, searches for them simultaneously, and combines the results.

For marketers, the practical lesson is to address the related questions that surround a buyer’s main question rather than assuming a single page or keyword captures the entire research journey.

Here’s what to do.

Build a fan-out content map without code.

Query fan-out is a technique that expands a single user question into related subqueries or subtopics that can be searched separately and then brought back together. Google explicitly documents this behavior for AI Mode; other AI products may implement query expansion differently.

Rios described the narrowing that can happen across a conversation using three examples — a local plumbing question, a regional HVAC purchase, and a broad family vacation question. He said, “It gets more narrow and narrow and narrow as the conversation continues with the LLMs.”

You can use that idea to build a content map without touching an embedding model:

1. Write down the core buyer question in the language a buyer would actually use.
2. Run it through several AI search experiences — ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews — and record any related questions, follow-ups, or subtopics the experience exposes.
3. Add the later-stage questions your sales team repeatedly hears from buyers. Those questions can reveal commercial concerns that generic keyword research misses.
4. Cluster the questions by meaning. Treat each cluster as a semantic neighborhood rather than assuming every variation needs its own page.
5. Assign each cluster a home: a dedicated page, a section within an existing page, or an FAQ answer. Anything important with no home is a potential coverage gap.

This can be a spreadsheet exercise rather than an engineering project. I use the resulting map alongside keyword research to decide what existing pages need deeper coverage and where genuinely new content is warranted.

Write for adjacent questions that answer engines expect.

Once you have the map, write for the adjacent questions a buyer is likely to ask next. Those questions may include:

• What does it cost, and what drives the cost up or down?
• How does it compare to the two obvious alternatives?
• What has to be true before this works, such as team size, prerequisites, or an existing stack?
• What goes wrong, and how do you know it’s going wrong?
• Who is this not for?
• How do you measure whether it worked?

Coverage is not the same as volume. Rios had come across a claim that it takes 250 pieces of content to become citable, but he doesn’t recommend chasing that number. His focus is on quality and trustworthiness rather than sheer output.

His position: “It’s not a matter of quantity. What the bad actors are trying to do is not have the quality, not [have] the trust score, and try to override it with massive amounts of noise.”

The practical takeaway is to prioritize useful depth over multiplying thin pages. One strong page can answer several related questions when those answers belong together; separate pages make more sense when the questions have genuinely different intent.

For a broader look at where search behavior is heading, read The Future of SEO: How People Will Get Their Questions Answered in 2+ Years.

How to Use Vector Embeddings in AEO to Measure AI Visibility

You can’t directly observe every retrieval decision an answer engine makes. What you can observe is the output: which prompts surface your brand, which pages get cited, how often competitors appear, and whether the description attached to your brand is accurate. Those signals can show you where to investigate, but they don’t prove that one content change caused a particular citation.

What to Track and How to Report Progress

Start with a baseline before you change anything, then compare the same prompts and metrics over a consistent period. Useful measurements include:

• Citation count and share: Track how often your pages are cited and how that compares with named competitors on the same prompt set.
• Cited URL distribution: Track which pages receive citations and whether visibility is concentrated on a small number of URLs. A broader distribution can be useful when your goal is to build visibility across multiple topics, but wider is not automatically better.
• Grounding queries: Bing Webmaster Tools’ AI Performance report shows a sample of the key phrases Bing AI used when retrieving content that was later referenced in AI-generated answers. Use those phrases as evidence about the queries connected with cited content, not as a definitive classification of your brand.
• Entity accuracy rate: Create a team-defined accuracy metric for answers that mention your brand. Check whether the category, current pricing, links, product details, and contact information are correct.
• Mentions versus citations: If your brand is named but a third-party publisher supplies the citation, investigate whether that pattern reflects an owned-content gap, the authority of third-party sources, or a distribution opportunity.
• Branded search volume and AI referral traffic: Use these as downstream signals alongside citation and visibility data rather than treating any one metric as proof of AEO performance.

Accuracy deserves its own review. An incorrect price, an outdated feature, or a dead link in an AI answer can mislead a buyer even when your visibility metrics look healthy.

When to Iterate Content Structure

When visibility is flat, diagnose the pattern before publishing more content.

Pro tip: There is no universal four- to eight-week waiting period across answer engines. Establish a baseline, keep the prompt set consistent, and compare trends over a window long enough to reduce day-to-day noise.

If you use HubSpot AEO, visibility tracking starts immediately and refreshes daily across ChatGPT, Perplexity, and Gemini; the first few weeks are directional, and most users start drawing reliable conclusions within a few weeks of consistent tracking.

Source

What Marketers Should Know About Embedding Models for AEO

There’s a difference between understanding how embeddings work and building with them. For most marketing teams, you can apply the content lessons before investing in embedding infrastructure.

Do you need a vector database to start?

For AEO content strategy, you do not need a vector database to start improving how clearly your content answers relevant questions. A vector database becomes useful when you’re building your own retrieval system, such as semantic search over a knowledge base, a support assistant that retrieves documentation, or another RAG application. Those are engineering projects, not prerequisites for publishing content that outside systems can discover.

If your technical team wants to explore your content with embeddings, it can embed a representative set of pages and compare their semantic similarity. That can help surface questions such as:

• Which pages cover substantially overlapping concepts?
• Where are the gaps between what buyers ask and what we’ve published?

That analysis does not reproduce the proprietary indexes, models, ranking signals, or retrieval pipelines used by commercial answer engines.

You can also do a rough qualitative version without embedding infrastructure. Compare two competing sections with a large language model and ask where their concepts overlap, or use monitoring tools such as HubSpot AEO to measure brand visibility, citations, competitor share of voice, and prompt performance over time.

How to Avoid Over-Engineering Early

Embeddings are interesting enough to pull a project toward infrastructure before you know whether infrastructure is the bottleneck. Start with the work that gives you a baseline and makes your existing content easier to understand:

1. Baseline your current AI visibility with a consistent set of prompts.
2. Review your highest-value commercial pages for clear, self-contained answers to buyer questions.
3. Write one core entity description and keep its category, audience, and differentiator consistent across the surfaces you control — on your site, in bios, on profiles, and in directories.
4. Build the fan-out map and fill the most important coverage gaps. Start with a simple spreadsheet.
5. Measure changes over a consistent window rather than reacting to a few days of volatility.
6. Then evaluate whether custom embedding or retrieval tooling solves a specific problem you can name.

Skip these, at least at the start of your AEO strategy.

• Rewriting your entire site by default: Start with the pages and passages tied to your highest-value questions, then expand when the evidence supports it.
• Building infrastructure before you have a baseline: Without a “before” measurement, it is harder to tell whether the tooling changed the outcome.
• Optimizing independently for every engine: Test meaningful platform differences, but avoid assuming each engine requires a completely separate content strategy.
• Treating paid placement as a substitute for organic relevance: AI advertising and organic citation solve different problems. Ad inventory inside AI interfaces is, in Rios’s words, “expensive in so many ways.”

Frequently Asked Questions About Vector Embeddings and AEO

Do I need a vector database for AEO?

No. A vector database is infrastructure you might use when building your own semantic-search or retrieval system. It is not a requirement for publishing content to be discoverable and retrievable by external answer engines. For marketers, start with clear, useful content and measurable visibility before considering custom retrieval infrastructure.

How do I write entity-first statements for my brand?

Use a subject-verb-object sentence with your brand name as the subject: “[Brand] is a [category] for [audience] that [differentiator].” Then keep the core category, audience, and differentiator consistent across your homepage, About page, author bios, social profiles, and relevant third-party profiles. Consistency reduces conflicting descriptions; it does not guarantee that every answer engine will resolve or describe the entity the same way.

What is the difference between embeddings and keywords?

Keyword or lexical search relies more heavily on the words and related terms present in the query and content. Embedding-based semantic search compares numerical representations of meaning, so it can identify relevant passages even when the wording differs. Modern retrieval systems can combine both approaches.

For example, a semantic system can recognize the relationship between “budget-conscious project tracking for small teams” and a question about “the cheapest tool for a five-person agency” even though the phrases are not identical.

How soon can I measure improvements in AI visibility?

There is no universal four- to eight-week waiting period across answer engines. Take a baseline before you make changes, keep your prompt set and measurement method consistent, and compare trends over enough time to reduce day-to-day volatility.

For HubSpot AEO specifically, HubSpot says visibility tracking starts immediately and refreshes daily across ChatGPT, Perplexity, and Gemini. The first few weeks are directional, and most users start drawing reliable conclusions within a few weeks of consistent tracking.

What Vector Embeddings Mean for Your AEO Strategy

Vector embeddings explain one important mechanism behind semantic retrieval, but they are not the whole AEO system. The useful lesson for marketers is simpler: make each important passage clear on its own, describe your brand consistently, cover the related questions buyers ask, and measure what answer engines actually surface and cite.

HubSpot AEO handles the measurement side by tracking visibility across ChatGPT, Gemini, and Perplexity, analyzing citations and competitor share of voice, and turning those signals into prioritized recommendations. The free AI Search Grader provides a one-time baseline of how your brand appears across those AI experiences.

Over the last year, I’ve spent months running citation tests across engines and kept landing on the same three signals: freshness, structure, and authority. What Rios and Kirkdorffer gave me was a useful way to think about why structure and consistent meaning matter. Since those recordings, I’ve stopped treating structure as a formatting preference. And if your brand is being described incorrectly in AI answers right now, accuracy deserves attention alongside visibility.

The State of AEO in 2026

Dive into data about how marketers around the world are adapting to AEO and learn how to implement AEO yourself.

• Optimizing for answer endings.
• Making purchases based on brands discovered through answer engines.
• Improving brand citation from doubling down on AEO.
• And more!

Access Now Learn more Access Now

Download Free

All fields are required.

Form not available

You're all set!

Click this link to access this resource at any time.

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