Category: RAG

Retrieval-Augmented Generation, often abbreviated as RAG, merges the capabilities of large language models with external data retrieval mechanisms. This blend allows AI systems to draw on updated, relevant information during response generation, improving the precision and depth of the output. Unlike conventional language models that rely solely on static, pre-trained knowledge, RAG systems can search […]