RAG (Retrieval-Augmented Generation)
A technique that enhances AI responses by retrieving relevant information from external knowledge sources.
Definition
Retrieval-Augmented Generation (RAG) combines the power of language models with real-time information retrieval. When you ask a question, the system first searches a knowledge base for relevant documents, then includes that information when generating a response.
RAG solves the problem of models having outdated or limited knowledge by grounding responses in current, specific information.
Why It Matters
RAG enables AI to provide accurate, up-to-date answers about your specific business, products, or domain. It reduces hallucinations by giving the model factual source material to work from.
Understanding RAG helps design knowledge systems that keep AI responses grounded in accurate, relevant information.
Examples in Practice
A company's internal AI assistant uses RAG to search policy documents before answering HR questions, ensuring responses reflect current procedures.
A legal AI searches case law databases with RAG, citing specific precedents rather than generating potentially inaccurate legal reasoning.
A product support bot retrieves relevant troubleshooting articles before responding, combining retrieved knowledge with natural language generation.