Embeddings
Numerical vector representations of text, images or other data where semantic similarity becomes mathematical proximity in a high-dimensional space.
· Reviewed by senior engineers
Embeddings are dense numerical vectors that represent the meaning of text, images, audio or other data. An embedding model takes input and outputs a vector — typically 384, 768, 1536 or 3072 dimensions — such that semantically similar inputs sit close together in vector space. "Dog" and "puppy" are close; "dog" and "bicycle" are far.
That property makes embeddings the workhorse of modern AI applications: semantic search, RAG, recommendation, clustering, classification, deduplication, anomaly detection. You don't need to train a new model for each — generate embeddings for your corpus once, then run cosine-similarity comparisons at query time.
The embedding model matters. OpenAI text-embedding-3, Cohere Embed v3, Voyage AI and open models like BGE and E5 all have different strengths on different domains and languages. The biggest mistake teams make is picking an embedding model on vibes rather than benchmarking it on their actual data and queries. MTEB is a useful starting point but never a replacement for a task-specific eval.
Devinsta benchmarks embedding models against client-specific evaluation sets before committing, then designs the pipeline so the model can be swapped without re-architecting downstream systems. Re-embedding a million documents is annoying; rebuilding the whole stack because you picked the wrong model is expensive.