Traditional NLP treated similarity as a surface game — matching words, stems, or syntax.

LLMs don’t work that way.

A model doesn’t ask:
“Do these sentences share vocabulary?”

It asks:
“Do these sentences share intent?”

Semantic similarity emerges from deeper structures:

• relational patterns

• implied goals

• conceptual roles

• emotional framing

• causal expectations

• narrative direction

Two sentences can look different and be nearly identical in meaning.
Two others can look identical and have nothing in common.

Similarity becomes cognitive, not lexical.

It’s why LLM-driven search feels like mind reading:
the model compares your intention to millions of conceptual templates learned during training.

To be visible, your content must signal clear, stable intention.
Ambiguity is the enemy of semantic similarity.