The phenomenon of hallucination in large language models, as discussed in the analysis by Nirdiamant (2025), exposes a structural tension at the core of contemporary generative systems: optimisation for plausibility rather than truth. Models such as GPT, Claude, or Gemini do not retrieve facts from a stable epistemic substrate; they predict statistically probable continuations of token sequences. Their fluency is therefore a function of pattern density within training distributions, not of ontological verification. The result is a system capable of authoritative tone without epistemic grounding—a confident improviser rather than a situated knower. Hallucination emerges not as anomaly but as structural consequence of probabilistic generation under conditions of informational uncertainty. Mitigation strategies—Retrieval-Augmented Generation (RAG), fine-tuning, prompt engineering, rule-based guardrails, uncertainty estimation, and self-reflective iteration—can be understood as attempts to graft external validation layers onto a generative core optimised for coherence rather than correspondence. RAG introduces factual anchoring; alignment and reinforcement learning temper speculative behaviour; guardrails impose deterministic checks; confidence scoring approximates epistemic humility. Yet none eliminates the foundational architecture: the model remains a predictive engine without intrinsic truth conditions. The broader implication is infrastructural rather than technical. Reliability in synthetic systems cannot depend solely upon model scale or benchmark optimisation; it requires executive environments capable of grounding, verifying, and constraining output. Hallucination is thus less a defect than a design feature of language prediction models. The task for developers and theorists alike is not to expect epistemic sovereignty from the model itself, but to construct surrounding architectures that transform probabilistic fluency into accountable knowledge production.
