You feed text to an embedding model (different from a chat LLM). Out comes a vector — typically 768, 1024, or 1536 dimensions. Cosine similarity between two vectors tells you how "similar" the underlying texts are.

Embedding Models 2026

OpenAI text-embedding-3-large: 3072 dim, top quality - OpenAI text-embedding-3-small: 1536 dim, cheap - Cohere embed-v3: multilingual strong - Multilingual e5-large: open-source, 1024 dim, Turkish/Arabic strong - BGE-large-v1.5: open-source, English For Turkish-heavy content: multilingual-e5 or Cohere multilingual.

Semantic search: find documents by meaning, not keyword - RAG: retrieve relevant chunks for LLM context - Deduplication: detect near-identical articles - Clustering: group support tickets by topic - Recommendation: "users who liked X also liked..."

OpenAI embedding-3-small: $0.02 per 1M tokens. Effectively free at small scale.

Embedding Best Practices

Normalize: use cosine similarity (not euclidean) - Chunk wisely: 200-500 tokens per chunk for RAG - Match query and corpus model: don't mix providers - Cache: embeddings are deterministic, save them

Read [what is RAG](https://vibecodingturkey.com/en/rehberler/what-is-rag) for the most common use case.

What Is an AI Embedding 2026 — Guide

What Is an AI Embedding 2026

An embedding is a list of numbers a vector that represents a piece of text in a way that captures meaning. Texts with similar meaning have similar vectors. Why They Matter Embeddings enable semantic search, RAG, clustering, classification, and recommendation. They turn unstructured text into something you can do math on. How They Work You feed text to an embedding model different from a chat LLM.…

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