Retrieval-Augmented Generation (RAG)
A technique that gives AI access to your specific data by retrieving relevant information before generating a response.
Full Definition
Retrieval-Augmented Generation (RAG) is a technique that makes AI models smarter by giving them access to external data sources at the time they generate a response. Instead of relying only on what the model learned during training, RAG first searches through a knowledge base. Your documents, database, website, or any collection of text. Finds the most relevant pieces, and feeds those to the AI along with the user's question. This solves two major problems with standard AI chatbots: they don't know about your specific company, products, or processes, and their training data has a cutoff date so they can't answer questions about recent events. RAG fixes both by connecting the AI to current, specific data. In practice, RAG powers most business AI chatbots and knowledge assistants. When you build a chatbot that answers questions about your company's documentation, you're building a RAG system. The AI retrieves relevant chunks of your docs, then generates an answer based on those specific passages rather than guessing from its general training.
Examples
A customer support chatbot that answers questions by searching through your help docs and product manuals, then generating a natural language response based on the relevant articles
An internal knowledge base assistant that searches through company policies, Slack messages, and meeting notes to answer employee questions
A legal research tool that searches through case law databases and generates summaries of relevant precedents for a specific question