RAG integrates traditional information retrieval techniques with generative AI, creating a hybrid approach that leverages the strengths of both.

RAG marries two fields: the precision of search and retrieval systems, which find relevant data quickly, and the creativity and language skills of large language models. This combination allows AI systems to access a broad range of external knowledge and use it flexibly in generation tasks, overcoming the static nature of fixed training datasets.