The content discusses the use of Vector Databases to enhance the accuracy and performance of Large Language Models (LLMs) through Retrieval Augmented Generation (RAG). It explains how feeding raw data into Vector Databases allows for precise encoding and decoding, leading to more nuanced answers from LLMs. The process involves converting text to numerical vectors, using Transformers for encoding and decoding, and loading data into a Vector Database like Pinecone. By embedding data into vectors, similarity searches can be performed to find relevant information. The content also touches on the complexity of analyzing large volumes of data and the benefits of using Vector Databases to streamline this process. Ultimately, the combination of Vector Databases and LLMs can lead to more accurate and detailed responses to queries.
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Source link: https://medium.com/@nandish.madhu/afghanistan-rag-australia-7e0e98c7856f?source=rss——llm-5
Afghanistan makes history with victory over Australia in RAG. #Cricket
