How to Fix AI Hallucination: The Nyquist Approach

How to Fix AI Hallucination: The Nyquist Approach By Mario Alexandre March 21, 2026 sinc-LLM Prompt Engineering Why Current Fixes Do Not Work The standard approach to AI hallucination is post-hoc: generate output, check it for errors, regenerate if needed. RAG (Retrieval-Augmented Generation) adds factual grounding. Fine-tuning adjusts model weights. These approaches treat symptoms, not the cause. The sinc-LLM paper identifies the root cause: hallucination is specification aliasing caused by undersampled prompts. The fix is not better retrieval or training, it is better sampling of the specification signal. Hallucination as Aliasing x(t) = Σ x(nT) · sinc((t - nT) / T) When a signal is sampled below its Nyquist rate, the reconstruction contains phantom frequencies, components that look real but were never in the original signal. This is aliasing. When a prompt is sampled below its specification Nyquist rate (6 bands), the LLM's output contains phantom specifications, constraints, contex