Retrieval-Augmented Generation (RAG) is a GenAI framework that enhances large language models (LLMs) by incorporating information from external knowledge bases, improving accuracy, relevance, and reliability of generated responses. Here's a more detailed explanation: What it is: RAG combines the strengths of both retrieval-based and generative AI models. It allows LLMs to access and incorporate information from external sources, like databases, documents, or web pages, to supplement their pre-existing training data. How it works: Retrieval: An information retrieval system searches external knowledge bases for relevant information based on the user's query. Augmentation: The retrieved information is then integrated into the LLM's input, providing it with context and factual grounding. Generation: The LLM uses this augmented input to generate a more accurate, relevant, and informed response.
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