Retrieval-Augmented Generation (RAG) is an approach in natural language processing (NLP) that combines retrieval mechanisms with text generation models to produce more accurate, relevant, and up-to-date responses.
- How it works: RAG involves retrieving information from an external knowledge base (like documents or databases) and feeding that context into a generative model (e.g., GPT) to create responses that are grounded in the retrieved data.
- Key Applications:
- Customer support: Crafting answers to queries by referencing company knowledge bases.
- Education: Generating answers to questions using trusted academic sources.
- Search engines: Providing concise, sourced, and context-aware responses.
RAG ensures that the generative outputs remain factually accurate and grounded in external, up-to-date information, bridging the gap between static generative models and real-world applications.
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