Embeddings

Embeddings are numerical representations that capture the meaning of text, images, or other data as vectors, letting AI systems compare and search content by similarity rather than exact keywords. They underpin technologies like semantic search, recommendation systems, and retrieval-augmented generation (RAG). This hub covers new embedding models, tools, and their use across AI applications.

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A simple diagram showing words placed as points in a vector space, illustrating how embeddings capture meaning as geometric relationships.

What Are Embeddings — and How Do They Let AI Understand Meaning?

27 July 2026

Embeddings turn words, images, and other data into numbers a computer can compare — the quiet technology behind AI search, recommendations, and chatbot memory.

t-SNE visualization of word embeddings clustering semantically similar words together in vector space

What Is a Vector Database — and Why Does AI Need One?

14 July 2026

Vector databases store AI embeddings and search by meaning rather than keywords. Here is how they work, and why they power tools like RAG-based chatbots and semantic search.