[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-embedding::en":3,"gloss-cluster-embedding::en":23,"gloss-next-embedding::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"embedding","core-ai","Embedding","An embedding is a fixed-length array of floating-point numbers — typically 256 to 3072 dimensions — produced by a neural network to represent the meaning of a piece of text, image, or audio in a way computers can compare mathematically. The core property that makes embeddings useful: semantically similar inputs produce vectors that are close together in that high-dimensional space, while unrelated inputs produce vectors that are far apart. This is the foundation of nearly every \"AI understands meaning\" feature in modern SaaS — semantic search, RAG retrieval, recommendation engines, clustering, deduplication, and content moderation classifiers. Embeddings are generated by specialized models (OpenAI's text-embedding-3, Cohere's embed-v3, Voyage AI, or open-weight options like BGE and E5) that are distinct from generation-focused LLMs — an embedding model's only job is to output a vector, not text. A concrete worked example: embedding the phrases \"cancel my subscription\" and \"how do I stop my monthly billing\" through the same embedding model produces two vectors with high cosine similarity (say, 0.89) even though they share almost no words — because the model has learned they mean nearly the same thing. Compare that to embedding \"cancel my subscription\" against \"what's the weather today,\" which would produce a low similarity score (say, 0.12). In practice, a builder calls an embeddings API like `POST https:\u002F\u002Fapi.openai.com\u002Fv1\u002Fembeddings {\"model\": \"text-embedding-3-small\", \"input\": \"cancel my subscription\"}` and receives back a JSON array of 1536 floats, which is then stored in a vector database for later similarity search. Embedding dimensionality and model choice matter: larger dimensions capture more nuance but cost more to store and search; and embeddings from different models are not directly comparable to each other, so a RAG pipeline must use the same embedding model for both indexing documents and embedding queries. One practical gotcha builders hit early: embeddings from different models (or even different versions of the same model) live in incompatible vector spaces — you cannot meaningfully compare an embedding produced by OpenAI's text-embedding-3 against one produced by Cohere's embed-v3, even though both are valid embeddings of text. This means switching embedding models requires re-embedding your entire document corpus from scratch, which is a real migration cost worth planning for before locking in a vendor at scale. Embedding models are also evaluated on public benchmarks like MTEB (Massive Text Embedding Benchmark), which is a reasonable starting point for comparing retrieval quality across providers before committing to one for a production RAG pipeline.","An embedding is a numeric vector representation of text (or images\u002Faudio) that captures semantic meaning, enabling similarity comparisons between concepts.",null,[11,14,17,20],{"slug":12,"name":13},"retrieval-augmented-generation","Retrieval-Augmented Generation (RAG)",{"slug":15,"name":16},"semantic-search","Semantic Search",{"slug":18,"name":19},"tokenizer","Tokenizer",{"slug":21,"name":22},"vector-database","Vector Database",[24,28,32,36,39,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":58,"category":5,"name":59,"updated_at":35},"deep-learning","Deep Learning",{"slug":61,"category":5,"name":62,"updated_at":27},"diffusion-model","Diffusion Model"]