How to use vector embeddings in AEO
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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. 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. Dive into data about how marketers around the world are adapting to AEO and learn how to implement AEO yourself. 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.” 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.
Source: HubSpot
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