Embedding Model

Embedding Model is a neural network that transforms raw data—words, sentences, images, audio—into dense numerical vectors whose distances encode semantic similarity. Trained with objectives such as contrastive learning, masked-language modeling, or triplet loss, it captures context and meaning so downstream systems can power semantic search, recommendation engines, clustering, and Retrieval-Augmented Generation (RAG). Popular examples include Sentence-BERT for text, CLIP for image–text pairs, and OpenAI text-embedding-3-large for cross-domain tasks. Quality depends on dimensionality, training corpus, and domain fit; performance is measured by recall@k, mean-average-precision, and clustering purity. Fine-tuning sharpens nuance, while quantization and pruning shrink model size for edge deployment. By turning human language and perception into machine-friendly math, embedding models are the backbone of modern AI pipelines.