Retrieval-Augmented Generation (RAG)

Gold definitionUpdated Apr 2, 2026

Definition

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases and incorporating it into the generation process. This allows LLMs to produce more accurate, up-to-date, and contextually grounded responses, reducing hallucinations.

At a glance

Executive summary

Retrieval-Augmented Generation (RAG) improves AI models, especially large language models, by letting them look up information from external sources before answering. This makes their responses more accurate, current, and reliable, preventing them from making things up.

TL;DR

RAG helps AI models give better answers by finding relevant information first, like a smart student checking their notes before speaking.

Key points

Combines information retrieval with large language model generation to provide contextually relevant responses.
Mitigates LLM hallucinations, provides access to up-to-date and domain-specific knowledge, and addresses knowledge bottlenecks.
Used by researchers, ML engineers, companies developing chatbots, search engines, and AI agent systems for specialized tasks like code generation and data visualization.
Unlike fine-tuning, which embeds knowledge directly into model weights, RAG keeps knowledge external, allowing for easier updates and transparency without retraining the entire LLM.
A rapidly evolving area focused on optimizing retriever components, integrating multi-modal data, and enhancing the reasoning capabilities of RAG systems.

Use cases

Powering internal company search engines that provide precise answers from proprietary documents, manuals, and reports.
Enabling customer support chatbots to answer complex customer queries accurately by retrieving information from product databases and FAQs.
Assisting healthcare professionals by providing evidence-based answers from vast medical literature and patient records.
Helping lawyers find relevant case law, statutes, and legal precedents quickly and accurately.
Aiding researchers in synthesizing information from scientific papers and databases to generate hypotheses or summarize findings.

Also known as

RAG-LLM, Retrieval-Augmented LMs, RAG models