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Retrieval-Augmented Generation (RAG)
A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer, producing more accurate and verifiable results.
Retrieval-Augmented Generation combines the power of large language models with real-time information retrieval. Instead of relying solely on training data, RAG systems search through your organization's documents, databases, and knowledge bases to find relevant context before generating a response. This produces answers that are grounded in your actual content, reducing hallucinations and ensuring up-to-date accuracy. For enterprises, RAG is the foundation of trustworthy AI assistants that can cite their sources and stay aligned with company knowledge.
Related terms
- Large Language Model (LLM) An AI system trained on massive amounts of text data that can understand, generate, and reason about human language with remarkable fluency.
- Vector Search A search technique that finds information based on meaning and semantic similarity rather than exact keyword matches, using mathematical representations (vectors) of text.
- Knowledge Base A centralized repository of organized information that employees can search and reference to find answers, procedures, policies, and institutional knowledge.
- AI Hallucination When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or not supported by its source data.