RAG-Enhanced Panorama of LLaMA 3(LLaMA 3 Practical 10)
Learn the fundamentals of Retrieval-Augmented Generation (RAG) and how LLaMA 3 enhances document segmentation, embedding, retrieval, and content generation for efficient AI applications.
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Welcome to the "LLaMA 3 Practical" Series
Starting from this lesson, we will officially discuss the knowledge related to RAG (Retrieval-Augmented Generation), which is one of the most frequently used techniques in the optimization of current large model applications.
First, let’s distinguish between search augmentation and retrieval augmentation. The search augmentation we learned earlier involves searching different possible generation paths during the model's inference process to achieve the optimal generation effect.
Retrieval augmentation, on the other hand, aids large language models in answering complex questions by leveraging external knowledge.
A common misconception in the retrieval augmentation process is the overemphasis on vector databases. Many people believe that relying solely on the "old three" — document segmentation, vector models, and vector databases — can solve retrieval issues.
However, while this approach has its value, by fully utilizing the capabilities of LLaMA 3, we can make the processes of indexing, retrieval, and content generation much smarter. Before delving deeper, let’s first understand the basic process of a simple RAG.