How to Reduce LLM API Costs by 97% with Structured Prompting

How to Reduce LLM API Costs by 97% with Structured Prompting By Mario Alexandre March 21, 2026 sinc-LLM Prompt Engineering The $1,500 Problem If you are running LLM-powered agents or applications in production, you have seen the bills. A typical multi-agent system processing thousands of requests per day can easily reach $1,500/month or more in API costs. The culprit is not the model pricing, it is the prompts. Raw, unstructured prompts waste tokens in three ways: they include irrelevant context, they force the model to generate exploratory output to compensate for missing specifications, and they require retry loops when the output does not match unstated expectations. The Signal Processing Solution x(t) = Σ x(nT) · sinc((t - nT) / T) The sinc-LLM paper applies the Nyquist-Shannon sampling theorem to prompt engineering. The core insight: a prompt is a specification signal with 6 frequency bands. Undersample it, and you get aliasing (hallucination) plus wasted tokens on compensation. S