Embeddings are often described as “vectors that represent words.”
Convenient, but deeply misleading.

An embedding doesn’t encode a word — it encodes the space of possibilities in which that word can take meaning.

LLMs don’t retrieve definitions. They reconstruct concepts every time they respond, combining contextual signals, relational patterns, and intent cues. That’s why a single token can shift interpretation across ten different prompts: the model isn’t recalling meaning; it’s building it dynamically.

Good embeddings are not “dense.”
They’re discriminative — capable of separating roles, functions, and implications.

It’s how a model distinguishes:

• apple the fruit

• Apple the corporate entity

• apple as a logical placeholder

Embeddings act as cognitive coordinates, not dictionary entries.

And this is the shift brands and creators often miss:
LLMs don’t reward keyword repetition — they reward semantic structure.

Visibility isn’t a match between strings.
Visibility is a match between intent maps.