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The proliferation of Large Language Models (LLMs) has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in text generation, summarisation, and complex reasoning.  However, their practical deployment in high-stakes applications is persistently undermined by a critical and inherent vulnerability: the tendency to "hallucinate," or generate content that is plausible and fluent yet factually incorrect, nonsensical, or un-grounded in the provided context. This phenomenon is not a simple bug or an occasional glitch in the system; it is a fundamental byproduct of the core design principles that govern these models. Understanding hallucination as an intrinsic property, rather than an anomaly, is the first and most crucial step toward developing effective and robust mitigation strategies.