Retrieval Augmented Generation (RAG) is a system design pattern that enhances Large Language Models (LLMs) by incorporating external knowledge. Instead of relying solely on the model's training data, RAG systems retrieve relevant information from documents and inject it into the prompt, leading to more accurate and grounded answers. This approach addresses common LLM issues like outdated knowledge, hallucinations, and the inability to access private or domain-specific data. The RAG architecture typically involves chunking documents, creating vector embeddings, storing them in a vector database, and then using similarity search to retrieve relevant context for the LLM. AI
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IMPACT RAG systems improve LLM accuracy and data access, enabling more customized and domain-specific AI applications.
RANK_REASON The article explains a technical concept (RAG) and its architecture, which is akin to a research paper or technical documentation. [lever_c_demoted from research: ic=1 ai=1.0]