Retrieval-Augmented Generation (RAG)
TL;DR
An AI architecture that enhances LLM responses by retrieving relevant information from a knowledge base before generating an answer.
Retrieval-Augmented Generation (RAG) is an AI system design pattern that combines the language capabilities of a large language model (LLM) with a real-time retrieval mechanism that fetches relevant documents from a private or specialised knowledge base before generating a response.
Without RAG, LLMs are limited to knowledge baked in during training — they can hallucinate facts, have knowledge cutoffs, and know nothing about your proprietary data. RAG solves this: when a user asks a question, the system first searches a vector database (like Pinecone or pgVector) for the most relevant documents, then passes those documents as context to the LLM, which generates a grounded, accurate answer.
RAG is commonly used to build enterprise chatbots trained on internal documentation, legal research tools, customer support systems connected to product FAQs, and research assistants that synthesise scientific literature. It's one of the most commercially valuable AI patterns in production today.
Examples in Practice
A customer support chatbot that answers questions using your actual product documentation. An internal HR assistant trained on company policies.