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Embedding
Phase 02 · LLM Fundamentalscore-concept

A dense vector (array of numbers) representing the meaning of text, allowing computers to compare concepts mathematically.

Why it exists

Computers cannot compare text strings for semantic meaning directly. Embeddings convert text into high-dimensional vectors where similar meanings sit close together.

How it works

An embedding model converts text into a fixed-length array of floats (e.g. 768 or 1536 dimensions). Measuring distance between vectors (via cosine similarity) reveals semantic closeness.

Where you'll use this