Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) connects a large language model to your own documents so it answers from verified sources instead of guessing. This hub explains how RAG works and where businesses use it.

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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.

What Is RAG (Retrieval-Augmented Generation) — and Why Do Businesses Use It?

5 July 2026

RAG connects a large language model to your own documents so it answers from verified sources instead of guessing. Here is how it works, where it is used, and how to get started.