Glossary · AI
Retrieval-Augmented Generation (RAG)
Grounding an LLM's answers in retrieved external documents.
What is Retrieval-Augmented Generation (RAG)?
RAG combines an LLM with a retrieval step that fetches relevant documents, so answers are grounded in a specific knowledge base rather than the model's memory alone.
RAG chatbots are a common AI app type and a natural fit for contextual monetization.
Retrieval-Augmented Generation (RAG) in context
To understand Retrieval-Augmented Generation (RAG) fully, it helps to know the concepts around it. Large Language Model (LLM), a neural network trained on large text corpora to generate and understand language. Prompt, the input text that instructs an LLM what to do. In-Conversation Ads, contextual ads placed inside an AI assistant's response stream rather than around the page.
Together these describe how ai works in practice for an AI app, and where Retrieval-Augmented Generation (RAG) fits among them.
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