Intermediate · Generative AI
Retrieval augmented generation (RAG)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. LLMs that can access and use external information to improve their responses.
Technical Definition
LLMs that can access and use external information to improve their responses.
How it works
Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by allowing them to dynamically retrieve relevant information from external knowledge bases. This retrieved information is then used to inform and ground the LLM's generated output. RAG helps LLMs provide more accurate, up-to-date, and contextually relevant responses.
Related Concepts
- Large Language Model (LLM) — A massive neural network trained on vast text corpora to understand and generate human language with remarkable fluency.
- Generative AI — An AI field focused on creating models that can generate novel and complex content, such as text, images, audio, and video, that is coherent and original.
- Information Extraction (IE) — Automatically identifying and extracting structured information from unstructured or semi-structured text.