RAG time in 2 steps: Pool and Water
🧠 RAG (Retrieval Augmented Generation) enhances AI response accuracy by grounding it in specific business data. Traditional computers fail to understand nuanced language, while AI translates semantic proximity into spatial vectors, even in thousands of dimensions. Large Language Models (LLMs) need guidance to avoid inaccuracies. By feeding LLMs with actual documents like HR policies or hotel information, RAG creates a personalized AI knowledge base, improving customer service and maintaining natural language understanding. The next episode will discuss Transformers in AI. The article also mentions the use of Perplexity.ai as a superior alternative to traditional search engines for complex queries.
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