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Retrieval-Augmented Generation (RAG) is an innovative approach in the field of natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text. This hybrid model leverages vast amounts of information available in large-scale databases or knowledge bases, making it particularly effective for tasks that require accurate and contextually relevant information1.
Components of RAG
RAG involves two main components:
Retriever: This component fetches relevant information from a large corpus or database. It is typically based on models like BERT (Bidirectional Encoder Representations from Transformers), which can effectively search and rank documents based on their relevance to the input query1.
Generator: This component takes the information retrieved by the retriever and generates coherent and contextually appropriate responses. The generator is usually a transformer-based model, such as GPT-3 or T5, known for its powerful language generation capabilities1.
What is RAG? - Retrieval-Augmented Generation AI Explained
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.
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