RAG: The Complete Guide to Retrieval-Augmented Generation 2026
RAG: The Complete Guide to Retrieval-Augmented Generation 2026 RAG combines LLMs with external knowledge retrieval to produce more accurate, up-to-date, and grounded responses. How RAG Works Indexing: Documents chunked and embedded into a vector database Retrieval: User query embedded and matched against stored vectors Augmentation: Retrieved context added to the LLM prompt Generation: LLM produces […]