The content discusses the limitations of Large Language Models (LLM) in providing domain-specific information, using an example of a Hamster mini app. LLMs are static and cannot provide answers beyond the dataset they were trained on, leading to inaccuracies. The solution to this is Retrieval Augmented Generation (RAG), which fetches data from external databases for updated information and context. RAG also helps LLMs cite sources and answer specific questions about businesses. Building foundation models like RAG is expensive, with estimates of $100 million to train models like ChatGPT. Not all companies have the resources for such projects due to talent scarcity, data labeling issues, and technical challenges. Incorporating RAG into LLMs can improve question and answer chatbots by providing context answers from company documents, reduce hallucination, enhance search engine segmentation, and enable easy questioning of data.
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Source link: https://medium.com/@RasheedOlaleye/introduction-to-rag-for-a-newbie-5a2b842fd58a?source=rss——llm-5
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RAG Introduction for Beginners: A Chef’s Quick Guide #RAGTutorial
Preeti
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