Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external authoritative knowledge bases before generating responses. LLMs, trained on extensive data with billions of parameters, handle tasks like Q&A and translation. RAG tailors LLMs to specific domains or internal knowledge without retraining, making it a cost-effective method for maintaining relevant, accurate, and useful output.

RAG offers benefits like cost-effective implementation, current information updates, enhanced user trust, and more developer control. It works by augmenting user input with retrieved data, improving the accuracy and relevance of responses.

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