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Ep. 02
Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of large language models (LLMs) with external knowledge sources. LLMs, while powerful, are limited by their training data, which can be outdated or incomplete. RAG addresses this by allowing LLMs to access external information, such as databases, knowledge bases, or websites. This enables LLMs to provide more accurate, up-to-date, and relevant responses, increasing user trust and confidence. RAG operates by converting user queries into numeric representations called embeddings, which are then compared to embeddings stored in a vector database. Relevant information is retrieved and presented to the LLM, which then generates a response. This process enhances LLMs with additional information and context, making them more versatile and useful across a wide range of applications.