RAG (Retrieval-Augmented Generation)
RAG is an AI technique enabling LLMs to retrieve info from external knowledge bases before generating responses to improve accuracy and reduce errors.
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI technique that enhances large language models by enabling them to retrieve relevant information from external knowledge bases before generating responses. Rather than relying solely on pre-trained knowledge, RAG systems search specific databases, documents, or websites to access current and accurate information, then use that retrieved context to produce more precise and factually grounded answers.
RAG provides several key benefits:
- Significantly reduces AI hallucinations by grounding responses in verified sources
- Accesses current, up-to-date information beyond the model's training data
- Enables source citation and attribution for transparency
- Powers many answer engines and AI search tools by combining AI reasoning with accurate information retrieval
This approach allows LLMs to reference up-to-date information and provide more reliable answers by combining the reasoning capabilities of AI with the accuracy of direct information retrieval.
Learn more: Explore how RAG improves AI accuracy in our Retrieval-Augmented Generation guide and AI Search Terms guide.
