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RAG is like giving an AI a library card—before answering, it looks up relevant information first, so its answers are more accurate and up to date.
Retrieval-Augmented Generation: A technique that enhances AI model responses by retrieving relevant information from external knowledge bases before generating answers.
RAG combines a retrieval system (typically using vector embeddings and similarity search) with an LLM generator. Documents are chunked, embedded, and stored in a vector database. At query time, relevant chunks are retrieved via semantic search and injected into the LLM prompt context to ground the generation in factual, source-specific data.