RAG
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May 1, 2024
3 minute read
Retrieval Augmented Generation (RAG) is a cutting-edge technique that enhances the capabilities of large language models (LLMs). By leveraging a dual-encoder architecture, RAG empowers LLMs with the ability to retrieve relevant information from extensive text databases, significantly improving their performance on various text-based tasks.
Understanding RAG
The core concept of RAG lies in its dual-encoder structure, consisting of a text encoder and a retrieval encoder. The text encoder converts the input text into a fixed-length representation, while the retrieval encoder transforms the text database into a collection of vectors.
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Find a path to becoming a RAG. Learn more at:
OpenCourser.com/topic/unox6c/ra
