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Understanding the Core Primitives of Large Language Models (LLMs)
In today’s rapidly evolving technological landscape, terms like Artificial Intelligence (AI) and Large Language Models (LLMs) are frequently tossed around in conversations, media, and corporate boardrooms. While these buzzwords capture the imagination and hint at groundbreaking advancements, they often come wrapped in a cloak of ambiguity and complexity. This can leave enthusiasts and professionals alike puzzled about what exactly these concepts entail.
The aim of this blog post is to cut through the jargon and demystify the fundamental components of LLMs and AI. By breaking down the core primitives that underpin these models, we hope to provide clarity and a deeper understanding of how these sophisticated systems function. Whether you’re an AI novice or someone looking to deepen your technical knowledge, this guide will illuminate the essential building blocks of Large Language Models.
Note: This blog post offers a high-level overview of each primitive, complemented by examples, without delving deeply into the underlying concepts. For a more comprehensive exploration, please refer to the list of references provided at the end of the post. These references include foundational and seminal papers, as well as authoritative sources, to further enhance your understanding and insights.
