RAG, or Retrieval-Augmented Generation, is a technique used in natural language processing (NLP) and machine learning. It combines two key components: retrieval of information and generation of text. The idea behind RAG is to enhance the generation of human-like text by first retrieving relevant documents or information from a large dataset, and then using this retrieved information to inform the generation process.
In a typical RAG setup, when a query or a prompt is given, the system first searches a large database (like Wikipedia or a similar corpus) to find relevant information. This information is then passed to a language generation model, such as a transformer-based model like GPT (Generative Pretrained Transformer), which uses this information to generate a response that is informed by the retrieved data.
This approach allows the system to produce more accurate, relevant, and informed responses, especially for queries that require specific knowledge or factual information. RAG models are particularly useful in applications like chatbots, question-answering systems, and other AI applications where providing contextually relevant and accurate information is crucial.
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3 окт 2024
