RAG (Retrieval-Augmented Generation)

RAG is an AI technique that allows large language models to access information from external knowledge sources before generating responses. This method enhances accuracy and reduces errors.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI method that improves language models by enabling them to retrieve pertinent information from external sources before producing responses. Instead of depending only on pre-trained knowledge, RAG systems search specific databases, documents, or websites to access current and precise data, using this context to deliver accurate and factually supported answers.

RAG offers several advantages:

• Minimizes AI hallucinations by grounding responses in verified sources
• Accesses the latest information beyond the model's training data
• Allows for source citation and transparency
• Powers answer engines and AI search tools by merging AI reasoning with precise information retrieval

This approach lets LLMs reference current information, enhancing reliability by combining AI's reasoning abilities with the precision of direct information retrieval.

Learn more: Discover how RAG enhances AI accuracy in our Retrieval-Augmented Generation guide and AI Search Terms guide.