: [R] Sinc Reconstruction for LLM Prompts: Applying Nyquist-Shannon to the Specification Axis (275 obs, 97% cost reduction, open source)

I applied the Nyquist-Shannon sampling theorem to LLM prompt engineering. The core finding: a raw prompt is 1 sample of a 6-band specification signal, producing aliasing (hallucination, hedging, structural incoherence). Key results from 275 production observations: - CONSTRAINTS band carries 42.7% of output quality - SNR improvement from 0.003 to 0.92 - 97% API cost reduction ($1,500 to $45/month) - All 4 optimized agents converge to identical zone allocation Paper: Code: pip install sinc-llm submitted by /u/Financial_Tailor7944 [link] [comments.