Convert text, images, and code into dense vectors that capture semantic meaning — the foundational primitive of modern AI systems.
Five passes over the same idea, each from a different angle. Do them in order, or jump to whichever you need.
Embeddings are the bridge between human language and mathematical space. A good embedding model maps "cat", "kitten", and "feline" close together while keeping "database" far away. The choice of embedding model — general-purpose vs. domain-specific, full-precision vs. quantised — dominates retrieval quality in RAG pipelines. This topic covers the theory, the benchmarks, and the production trade-offs.
