2 docs tagged with "embeddings"

How embedding models work, the 2026 model landscape, how to choose one, dimensions and Matryoshka, vector quantization, domain adaptation, hybrid retrieval, and the production pitfalls that quietly halve recall.

How retrieval-augmented generation grounds an LLM in external knowledge using embeddings and vector databases, how it compares to fine-tuning, and the production levers that make it work.