// AI
RAG
Also known as Retrieval-Augmented Generation
Retrieval-Augmented Generation, grounding an LLM's response in retrieved documents to reduce hallucination.
In depth
The default architecture for enterprise AI in 2024–2026: embed the corpus (CRM notes, docs, calls) into a vector DB, retrieve the top-k relevant chunks at query time, and prompt the LLM with them. Every 'AI assistant in your CRM' is a RAG pipeline.
Related terms
LLM
Large Language Model, the transformer-based foundation model behind ChatGPT, Claude, Gemini and every 2024+ AI product.
Embedding
A dense numeric vector representation of text, image or audio, the input format for semantic search and RAG.
Vector Database
A database that stores and searches high-dimensional embeddings, the retrieval layer of every RAG pipeline.
Hallucination
When an LLM generates plausible-sounding but factually wrong output, the fundamental risk of generative AI.