LLM Prompt Optimization: From 80,000 Tokens to 2,500

LLM Prompt Optimization: From 80,000 Tokens to 2,500 By Mario Alexandre March 21, 2026 sinc-LLM Prompt Engineering The Token Bloat Problem Production LLM systems accumulate token bloat over time. Context windows fill with conversation history, system prompts grow with edge-case patches, and retry loops multiply the effective token cost per task. A multi-agent system that started at 5,000 tokens per request can reach 80,000 within months. The sinc-LLM paper documented this progression across 11 production agents and found that 97% of tokens in bloated prompts were noise, information that did not contribute to output quality. Signal-to-Noise Ratio for Prompts x(t) = Σ x(nT) · sinc((t - nT) / T) The paper introduces Signal-to-Noise Ratio (SNR) as a metric for prompt efficiency. SNR is calculated as the ratio of specification-relevant tokens to total tokens. The findings across 275 observations: Mode Input Tokens SNR Monthly Cost Unoptimized (sliding window) 80,000 0.003 $1,500 Enhanced (b