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Embeddings help AI retrieve information by turning text into lists of numbers that capture meaning: a question and a passage […]

A vector database is a database designed to store embeddings—long lists of numbers that represent the meaning-related features of text, […]

An AI knowledge base is a curated collection of documents, records, or web pages that an AI system searches when […]

When an AI system looks something up, the retrieved text is placed directly in front of the model, in the […]

No AI platform publishes a scorecard that grades your website. When ChatGPT, Google’s AI answers, or Perplexity choose sources, there […]

AI systems get information from websites the unglamorous way: automated programs called crawlers visit pages and copy them, the copies […]

RAG — retrieval-augmented generation — works in two halves. Before any question is asked, a collection of documents is split […]

The short answer Retrieval-augmented generation — RAG — is a way of building AI systems in which the model does […]

The short answer An AI system draws on three reservoirs when it answers a question: knowledge absorbed during training, information […]