RAG Explained: How Retrieval-Augmented Generation Works

Retrieval-Augmented Generation (RAG) is a key architectural pattern for LLM applications, designed to overcome limitations like knowledge cutoffs and hallucinations. RAG works by first retrieving relevant information from an external knowledge base and then using that information to inform the LLM's response. The process involves an offline indexing phase where documents are chunked, embedded into vectors, and stored in a vector database, followed by an online query phase where user queries are embedded and used to find similar document chunks for the LLM to generate an answer. AI

IMPACT Explains a core technique for enhancing LLM capabilities with external data, crucial for practical AI applications.

RANK_REASON This article explains a technical concept (RAG) and its workflow, akin to a technical paper or tutorial. [lever_c_demoted from research: ic=1 ai=1.0]

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