Retrieval Augmented Generation (RAG)
A technique that grounds AI responses in retrieved documents for accurate, cited answers.
Definition
Retrieval Augmented Generation (RAG) is a technique that combines information retrieval with text generation. Instead of relying solely on a language model's training data, RAG first retrieves relevant documents from a knowledge base, then uses those documents as context for generating responses. This grounds AI answers in actual source material, reducing hallucination and enabling citation of sources.
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Related terms
Large Language Model (LLM)
AI models trained on vast text data to understand and generate human language.
Semantic Search
Search that understands the meaning of queries rather than just matching keywords.
Enterprise AI
AI systems designed for business use with appropriate security, governance, and integration capabilities.
More in Core Concepts
Grounding
Connecting AI responses to verified source documents to ensure accuracy.
Hybrid Search
Combining keyword and semantic search for more comprehensive results.
Semantic Search
Search that understands the meaning of queries rather than just matching keywords.
AI Citations
References that trace AI-generated answers back to source documents.
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