RAG

RAG (Retrieval-Augmented Generation) connects LLM reasoning with the breadth of an external knowledge base: a retriever finds relevant chunks, a generator receives them as context and responds. Enables use of knowledge post-training cutoff date and proprietary domain knowledge without retraining. Pipeline: document chunking → embedding/indexing → retrieval → context augmentation → generation. Retriever quality defines the ceiling: if chunks aren’t found, even the best LLM is powerless.

Related: [Document Chunking], [Hybrid Search], [Agentic RAG]