Retrieval-Augmented Generation (RAG) is a technique that enhances large language models (LLMs) by integrating real-time data retrieval from external sources like FAQs or documentation. Unlike static LLMs, which struggle to keep updated with frequently changing information, RAG allows models to pull in relevant content on-the-fly, improving the accuracy and relevance of generated responses. By processing and chunking content into manageable pieces and providing it as context, RAG enables chatbots to offer timely and precise answers, thus fulfilling user needs effectively.
Chunking the content involves breaking down materials into smaller, manageable pieces, allowing language models to process and retrieve relevant information efficiently for generating responses.
RAG enhances LLM functionality by merging it with external data, enabling real-time updates and ensuring the chatbot provides accurate responses based on the latest information available.
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