Retrieval-Augmented Generation (RAG) is a technique that optimizes large language models (LLMs) by combining their natural language processing capabilities with real-time access to external data sources. This is particularly useful for applications like chatbots, where frequently updated FAQs are involved. Because LLMs are typically trained on static datasets, they may not be aware of recent updates. RAG solves this by allowing the retrieval of the latest relevant documents from a knowledge base, which can then be used to inform model responses, ensuring accuracy and relevance in real-time conversations.
As a conversation designer, it's important to understand some of the techniques used to optimize large language models (LLMs).
RAG stands for Retrieval-Augmented Generation, a technique that combines natural language capabilities of LLMs with external data to enhance accuracy.
Instead of relying solely on what the model 'knows', RAG retrieves relevant documents from your knowledge base in real time.
The process of RAG involves chunking content, retrieving relevant info, augmenting the model, and generating a final response.
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