Numerical representations of text (or images, audio) as points in a high-dimensional space, where semantically similar content lives near other similar content.
A vector embedding is a list of numbers, typically between 384 and 3,072 dimensions, that represents the semantic meaning of a piece of content. Embeddings are produced by neural networks trained so that texts with similar meaning end up near each other in vector space, while unrelated texts end up far apart.
In practice, embeddings power semantic search and retrieval-augmented generation. When an AI search engine wants to find relevant documents for a user query, it embeds the query, embeds all the candidate documents, and returns the documents whose embeddings are closest to the query embedding (usually measured by cosine similarity).
Traditional keyword search only matches literal word overlap. Embedding-based search matches meaning. A query for "car" can retrieve a document about "automobiles" even if the word "car" never appears, because the embeddings for both concepts live near each other in vector space.
This changes optimization strategy. Instead of stuffing a page with keyword variations, the goal is to clearly express the underlying concepts. LLMs embedding your content will place it near all the queries that share its meaning, which is a much broader and more forgiving target than exact-match keywords.
Acta AI writes in natural, concept-rich language rather than keyword-padded copy. This produces embeddings that cluster near the full space of related queries, not just the exact keyword targeted. The Acta Score GEO Citability dimension rewards this semantic clarity as part of its evaluation.
Embeddings are abstract numbers, but the similarity between them maps to intuitive meaning. A typical embedding call looks like this:
from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
model="text-embedding-3-small",
input="Generative Engine Optimization"
)
vector = response.data[0].embedding
# vector is a list of 1,536 floats:
# [0.0123, -0.0456, 0.0789, ..., 0.0234]
# Two embeddings can be compared with cosine similarity.
# "GEO" vs "AI search optimization" -> similarity 0.89
# "GEO" vs "best pizza in Rome" -> similarity 0.12The similarity scores are what retrieval systems use to decide which pages to feed an LLM. Pages with embeddings close to the query embedding get pulled in. Pages with distant embeddings never make it to the generation step.
Every article on our blog was written by Acta AI. No edits. No ghostwriter.
Read Our BlogStart Free Trial