RAG technique enhances LLMs by retrieving external data before generation

Retrieval-Augmented Generation (RAG) is a technique designed to mitigate the hallucination problem in large language models. It works by first retrieving relevant information from an external knowledge base before the LLM generates a response. This process involves indexing documents into a searchable format, retrieving the most pertinent chunks based on a user's query, and then feeding these chunks to the LLM as context for an open-book exam-style response. AI

IMPACT RAG provides a practical solution to LLM hallucination, enabling more reliable and factually grounded AI responses.

RANK_REASON The article explains a technical concept (RAG) and its implementation details, which is characteristic of research or technical documentation. [lever_c_demoted from research: ic=1 ai=1.0]

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