In this article, we’ll explore three core concepts that have a key impact on the working of LLM

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A conceptual explainer covering three foundational mechanisms behind how large language models are trained: loss functions (measuring model error), gradient descent (iteratively adjusting parameters to reduce loss), and next-token prediction (the core training task). The piece clarifies that LLMs are sophisticated pattern-matching systems, not reasoning engines, and explains why this distinction leads to predictable failure modes such as hallucinations, poor performance on novel problems, and inability to handle modified logic puzzles. Practical guidance is offered on when to trust LLM outputs and when to remain skeptical.

How Large Language Models Learn

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Why This Is Amazing But Also Has Problems