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Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is an AI framework for improving the quality of responses generated by large language models (LLMs) by grounding the model on external sources of information. RAG uses semantic search to retrieve relevant and up-to-date information from a wide range of sources, including books, articles, websites, and databases. This information is then used to inform and improve the text generation of the LLM.

RAG has several advantages over traditional language models.

  • First, it can provide more accurate and up-to-date responses, as it is able to access the latest information.
  • Second, it can reduce the risk of generating erroneous or misleading content, as it is grounded on a verified knowledge base.
  • Finally, RAG can be used to generate different creative text formats, such as poems, code, scripts, musical pieces, emails, and letters.