SEO used to be about “Keywords.” Now it is about “Vectors.” But what does that mean?

In the Agentic Web, search engines don’t just match strings (“shoes” == “shoes”). They match concepts in a high-dimensional geometric space.

The Vector Space

Imagine a 3D graph (X, Y, Z).

  • “King” is at coordinate [1, 1, 1].
  • “Queen” is at [1, 1, 0.9]. (Very close distance).
  • “Apple” is at [9, 9, 9]. (Far away).

Modern LLMs use thousands of dimensions (e.g., OpenAI’s text-embedding-3 uses 1536 dimensions). Every product description, blog post, or review you write is turned into a single coordinate in this massive hyper-space.

Vector Distance (Cosine Similarity)

When a user searches, their query is turned into a coordinate. The search engine looks for the documents that are geometrically closest to that coordinate.

Metric: Cosine Similarity (the angle between vectors).

  • 1.0: Perfect Match (Identical semantics).
  • 0.8: High Relevance (Synonymous or highly related).
  • 0.5: Vague Relevance (Same broad topic).

E-Commerce Query Matching Example

Let’s look at how a vector engine processes a query versus a keyword engine.

Query: “Red dress” vs “Crimson gown”

Keyword Engine:

  • Looks for string “Red” AND “Dress”.
  • Misses “Crimson” (unless explicitly synonymized).
  • Misses “Gown” (unless explicitly synonymized).

Vector Engine:

  • The vector for “Red” is mathematically very close to “Crimson” (Distance: 0.12).
  • The vector for “Dress” is very close to “Gown” (Distance: 0.15).
  • Therefore, the vector for “Red dress” overlaps heavily with “Crimson gown”.

The search engine knows they are the same thing without a synonym list. It knows this because in its training data, “Crimson” and “Red” appear in similar contexts.

Optimizing for Distance

To rank, you need to minimize the distance between your content vector and the user’s intent vector.

How?

  1. Cover the Whole Topic (The “Wider” Vector): A comprehensive article has a “wider” vector footprint that overlaps with more user queries. If you sell cameras, don’t just list specs. Discuss “low light performance,” “shutter speed,” and “portrait photography.” This pulls your vector towards those intent clusters.

  2. Use Canonical Vocabulary: Use the specific terms the industry uses. If users search for “Attribution,” and you call it “Source Tracking,” your vectors might be slightly misaligned. While modern models are good at synonyms, precision still wins. The distance between “Source Tracking” and “Attribution” is non-zero. Minimize that gap.

  3. Avoid Drift (The “Average” Trap): Don’t go off-topic. If 50% of your article is unrelated anecdotes, your vector gets pulled “average” towards the center of the graph, making it less specific to the query. A product page for a “Gaming Laptop” that spends 300 words talking about the history of video games is diluting its vector. Stay on target. Keep your vector tight.

The “Negative Vector” Strategy

Sometimes, you need to tell the model what you are not. In vector space, meaning is defined by position relative to other concepts. To clarify your position, explicitly distance yourself from adjacent but incorrect concepts.

  • “We are not a consulting agency; we are a SaaS Platform.”

This sentence applies a mathematical force vector that pushes your entity away from the “Agency” cluster and towards the “Software” cluster. Use “Not” statements strategically to refine your coordinates.