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Current LLM-based AI models, built on stochastic neural networks, can't truly 'know' what they're doing. They lack the deterministic, logic-based systems needed for true reasoning and reliable output. This fundamental flaw hinders trust and progress. Even if they *wanted* to provide this behavior, they wouldn't be able to because the core architecture isn't designed nor does it support the right logical, semantics, knowledge base that is needed to provide such trustworthy and reliable AI. but DI
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RAG stands for Retrieval-Augmented Generation. It helps an LLM answer using external knowledge such as company documents, internal policies, research papers, or private data that the model may not already know. Here is how it works: Documents are split into smaller pieces called chunks. When a user asks a question, the system searches those chunks and retrieves only the most relevant information. That information is added to the prompt, and the LLM uses it to generate the answer. So the basic fl
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