Embeddings: How AI Represents Meaning

What an Embedding Is

An embedding is a high-dimensional vector that represents semantic meaning.

It does not store words. It stores relationships.

When you embed the word "king," you get a vector of numbers (typically 768 to 4096 dimensions). That vector encodes the word's relationship to every other concept the model has learned.

From Tokens to Vectors

After tokenization, each token passes through an embedding layer:

1. Token ID enters the model
2. Embedding layer maps it to a dense vector
3. Vector represents the token's "semantic potential"

At this stage, meaning is not yet fixed. The vector captures what the token could mean in various contexts.

Contextual Embeddings

The same word produces different embeddings depending on context.

Example:

This is why modern LLMs use contextual embeddings (via transformers) rather than static word vectors. Context determines the final vector.

Embeddings and Similarity

Embeddings are compared using distance metrics such as cosine similarity.

How it works:

• Two vectors pointing in similar directions = semantically related
• Cosine similarity of 1.0 = identical meaning
• Cosine similarity of 0.0 = unrelated

This is the foundation of semantic search and RAG (Retrieval Augmented Generation).

Why Embeddings Matter for AI Visibility

If your content:

• Lacks explicit context
• Uses vague claims
• Mixes unrelated entities
• Relies on assumed knowledge

Then its embedding becomes unstable.

Unstable embeddings are skipped during retrieval.

When an AI system searches for relevant content, it compares query embeddings against document embeddings. If your content's embedding is ambiguous, it won't match strongly against any query.

What Makes an Embedding Strong

Strong embeddings come from:

• Clear entity definitions
• Explicit relationships stated in text
• Consistent terminology
• Concrete claims with context

Weak embeddings come from:

• Marketing fluff ("innovative solutions")
• Undefined acronyms
• Context-dependent references without context
• Mixed signals in the same passage

Practical Example

Weak (unstable embedding):

"We help companies unlock their potential with cutting-edge technology."

The embedding for this sentence will be generic. It could match almost any tech company. AI has no reason to prefer this content over thousands of similar statements.

Strong (stable embedding):

"Gong is a revenue intelligence platform that records sales calls, transcribes conversations, and identifies winning patterns. Used by 4,900+ B2B companies."

This produces a focused embedding that will surface for relevant queries about revenue intelligence, sales call analysis, or conversation recording.

Key Takeaway

AI does not understand brands. It understands vectors.

Your content competes in embedding space, not mindshare. If your vectors are weak, you don't exist to AI.