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.
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