Embeddings Explained for Your Name: How Your Bio Becomes Vectors

Your bio does not get matched against a query as text. It gets matched as a point in space, and the words you chose decided exactly where that point sits.

What an embedding actually is

An embedding is a fixed-length list of numbers, commonly a few hundred to a few thousand values, produced by a model trained specifically to place semantically similar text close together in that numeric space and dissimilar text far apart. A sentence like "she consults on HIPAA compliance for clinics" and a sentence like "she helps healthcare practices pass privacy audits" describe closely related ideas in different words, and a well-trained embedding model places their vectors near each other despite sharing almost no exact words in common. That is the entire point of the technique: it captures meaning, not just vocabulary overlap.

Every embedding model defines its own space, with its own number of dimensions and its own notion of what counts as similar, so the same sentence produces a different vector under a different model. This is invisible to you as a website owner, but it explains why the same page can retrieve well in one AI product and poorly in another: different products often use different embedding models under the hood as part of the retrieval pipeline described in how RAG retrieval works.

How your text becomes a vector, mechanically

The process runs in three rough steps. First, tokenization splits your text into small units, often word pieces rather than whole words. Second, the embedding model processes those tokens through several layers that build up a contextual representation, meaning the same word can contribute differently to the final vector depending on the words around it. Third, the model pools that contextual representation into one fixed-length vector representing the entire passage, which is the number list actually stored and compared at retrieval time.

The contextual part matters more than it sounds. The word "consultant" contributes a different signal in "independent HIPAA compliance consultant for healthcare clinics" than it does in "consultant" alone with no supporting context, because the surrounding words shape what the model understands the term to mean in that specific sentence. This is one reason a title alone, with no descriptive sentence around it, embeds far more weakly than the same title used inside a complete, specific claim.

A concrete before-and-after example

The strong version wins not because it is better writing in a literary sense, but because its vector sits near a narrow, specific region of the space that real queries actually land in.

How closeness gets measured

Once text exists as vectors, comparing a query to a candidate passage is a matter of measuring how close their two vectors sit, most commonly using cosine similarity, a calculation of the angle between two vectors rather than their raw distance. A conceptual sketch of the comparison:

# conceptual, not literal implementation
query_vector = embed("who does HIPAA audits for small clinics")
candidate_vector = embed("Your Name helps small healthcare
                          clinics pass HIPAA compliance audits")

similarity = cosine_similarity(query_vector, candidate_vector)
# closer to 1.0 means the two pieces of text sit near each
# other in meaning-space; closer to 0 means they are largely
# unrelated as far as the embedding model is concerned

Retrieval systems run this comparison against many candidate passages and return the highest-scoring ones as context for the generation step. Nothing in this comparison cares about keyword density or exact phrase matches the way older search algorithms did; it cares whether the meaning of your sentence sits close to the meaning of the question being asked.

Why structure changes the resulting vector

Because embeddings are typically computed per chunk rather than per whole page, how you break your content into passages directly affects what gets embedded and compared. A single sentence buried in the middle of a long paragraph that also covers three unrelated topics gets pooled into one vector representing a blurry mix of all three topics, diluting the specific claim you wanted retrieved. A clean, isolated sentence or short paragraph making one specific claim embeds as a sharper, more targeted vector. This is the embedding-level explanation for why chunk theory and passage optimization matters as much as it does: good chunking is really just good embedding hygiene.

The writing discipline this implies overlaps directly with the general craft of answer-shaped writing: specific, self-contained, query-language sentences serve both the embedding comparison covered here and the human reader deciding whether your page actually answered their question.

Practical writing rules that follow from this mechanism

• Write one clear claim per sentence or short passage. Mixing three ideas into one paragraph blurs the resulting vector across all three.
• Use the vocabulary a real query would use. "HIPAA compliance audits" embeds closer to real questions than "regulatory excellence" ever will, no matter how accurate the latter feels as a description.
• Give the title supporting context. A bare job title embeds weakly; the same title inside a specific, descriptive sentence embeds strongly.
• Repeat key facts in more than one place, in slightly different phrasing. Different embedding models react differently to phrasing, so several honest restatements of the same fact improve the odds of a close match across more than one system.
• Avoid burying a specific claim inside a long, multi-topic paragraph. Give it its own sentence or its own short section so it can be chunked and embedded cleanly.

Why marketing language systematically underperforms in this system

It is worth being explicit about why adjective-heavy marketing writing loses in this specific mechanism, because the reason is structural rather than a matter of taste. Words like "innovative," "strategic," and "results-driven" appear in an enormous volume of unrelated professional bios across the entire web that the embedding model was trained on, which means they carry very little discriminating signal. A model has seen those words attached to thousands of different professions and specialties, so a sentence built mostly out of them produces a vector that sits in a crowded, generic region of the space, one shared by an enormous number of other people's bios. A sentence built out of specific, technical, industry-precise language, "vendor risk assessment," "supplier concentration," "single-source manufacturing," occupies a much less crowded region, because comparatively few pages use that exact combination of terms. Fewer competing neighbors in that region of the vector space means a much higher chance that a query landing nearby finds you specifically rather than finding you blended into an indistinguishable crowd.

Where this connects back to schema and structured data

Embeddings are computed from raw text, not from your JSON-LD markup, which is worth stating plainly since it is a common point of confusion. Schema properties like knowsAbout, covered in the knowsAbout property explained, do not feed directly into the embedding calculation the way your visible prose does. What schema does instead is help a system resolve which entity a retrieved passage belongs to once retrieval has already happened, a separate and complementary job. The practical implication is that you cannot substitute good schema for good prose when it comes to retrieval performance; you need both, doing two different jobs in the same pipeline.

Common mistakes

• Writing only in brand voice, never in query language. A bio that reads beautifully to a human but never uses the words a buyer would actually search embeds far from where it needs to sit.
• One giant paragraph covering an entire career. Specific, valuable claims get diluted inside a vector that represents the paragraph as a whole rather than any one fact clearly.
• Assuming one well-written page is enough everywhere. Different products use different embedding models, so a page that retrieves well on one system is not guaranteed to retrieve well on another.
• Ignoring headings and structure. Clear headings help both human readers and chunking systems isolate distinct claims into their own passages, which improves the resulting embeddings.

You cannot see your own embeddings directly, but you can test the proxy

Most website owners have no direct access to the embedding vectors a given AI product computes for their pages. What you can do instead is a practical proxy test: write down five to ten real questions a buyer might ask about you, then check whether your own page, read cold with no other context, actually answers each one in a self-contained sentence or two. If a human reading only that sentence would say "yes, that answers it," the passage is very likely to embed usefully as well, since the mechanism rewards exactly the same clarity a human reader rewards.