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Retrieval Augmented Generation (RAG)

Description

Retrieval Augmented Generation (RAG) enhances language model capabilities by integrating an information retrieval component with a text generator. This combination enables models to access external knowledge sources, improving factual accuracy and reliability.

  • Dynamic Knowledge Access: RAG allows models to adapt to new information without needing complete retraining.
  • Factual Consistency: By leveraging external data, RAG reduces the chances of generating incorrect or "hallucinated" content.

Workflow

  • Input Processing: RAG takes user input and retrieves relevant documents from a knowledge source (e.g., Wikipedia).
  • Document Integration: The retrieved documents are combined with the input prompt.
  • Text Generation: The augmented prompt is processed by the language model to generate a final output.